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SEO","\u002Fportfolio\u002Fseo-sem-branding-mobile-webseite-muenchen",[],{"id":194,"title":195,"url":203,"target":61,"icon":172,"isActive":14,"type":173,"productId":10,"categoryId":10,"shopCategoryId":10,"articleId":10,"pageId":10,"portfolioId":10,"children":204},"item-31",{"de":196,"en":197,"es":198,"fr":199,"it":200,"ru":201,"sr":202,"zh":197},"Digitalisierungsportal","Digitalization Portal","Portal de digitalización","Portail de numérisation","Portale di digitalizzazione","Портал цифровизации","Портал за дигитализацију","\u002Fportfolio\u002Fdigitalisierungsportal-archiv-museum-bibliothek-ead-lido-mets-mods",[],{"statusCode":4,"data":206,"message":2229},{"id":207,"title":208,"slug":209,"content":210,"contentJson":211,"excerpt":1045,"featuredImage":1046,"featuredImageAlt":1047,"featuredImageCaption":10,"featuredImageTitle":10,"featuredImageCopyright":10,"featuredImageAuthor":10,"featuredImageSourceUrl":10,"featuredImageLicense":10,"featuredImageIsAiGenerated":43,"status":1048,"publishedAt":1049,"createdAt":1050,"updatedAt":1051,"seoLocalePaths":1052,"categories":1061,"author":1085,"translations":1090},"480","¿Cuándo debería una IA dejar de confiar en su propio conocimiento? — El desencadenante de la recuperación","when-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u003Ch2 id=\"section-1\">Pregunta\u003C\u002Fh2>\n\u003Cp>¿Cuándo debería una IA dejar de confiar en lo que ya sabe y recuperar información externa antes de responder?\u003C\u002Fp>\n\u003Cp>Esta pregunta parece simple, pero se sitúa en el centro de una de las decisiones de diseño más importantes en los sistemas de IA modernos.\u003C\u002Fp>\n\u003Cp>Los grandes modelos de lenguaje contienen un conocimiento sustancial en sus parámetros. La Generación Aumentada por Recuperación añade información externa en tiempo de ejecución. Pero ninguno de los extremos es ideal.\u003C\u002Fp>\n\u003Cp>Confiar siempre en el modelo puede producir respuestas desactualizadas o sin respaldo. Recuperar información siempre añade latencia, costo, contexto irrelevante y nuevas oportunidades para errores de recuperación.\u003C\u002Fp>\n\u003Cp>El verdadero problema, por lo tanto, viene antes de RAG: ¿Cuándo debería ocurrir la recuperación en absoluto?\u003C\u002Fp>\n\u003Cp>Este artículo utiliza el término Disparador de Recuperación para esa decisión. El Disparador de Recuperación no se presenta aquí como un término estandarizado de la literatura de investigación. Es un concepto práctico de sistemas que reúne ideas ya visibles en la investigación sobre recuperación activa, adaptativa y autorreflexiva.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Un Disparador de Recuperación es una condición que indica que un sistema de IA debería dejar de confiar únicamente en el conocimiento interno del modelo y obtener evidencia externa antes de producir o finalizar una respuesta.\u003Ccite class=\"block mt-2 text-sm\">— Definición de trabajo\u003C\u002Fcite>\u003C\u002Fblockquote>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Contenido\">\u003Cstrong class=\"editorjs-toc__title\">Contenido\u003C\u002Fstrong>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-0\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-1\" class=\"editorjs-toc__link\">Pregunta\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-10\" class=\"editorjs-toc__link\">Qué significa esto realmente\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-20\" class=\"editorjs-toc__link\">Ejemplo más simple\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-33\" class=\"editorjs-toc__link\">Dónde el ejemplo deja de funcionar\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-43\" class=\"editorjs-toc__link\">Respuesta directa\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-48\" class=\"editorjs-toc__link\">Por qué esto es así\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-55\" class=\"editorjs-toc__link\">Contexto\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-65\" class=\"editorjs-toc__link\">Supuestos\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-71\" class=\"editorjs-toc__link\">Variables\u003C\u002Fa>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-1\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-73\" class=\"editorjs-toc__link\">Frescura\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-75\" class=\"editorjs-toc__link\">Especificidad\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-77\" class=\"editorjs-toc__link\">Requisito de evidencia\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-79\" class=\"editorjs-toc__link\">Cobertura del conocimiento\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-81\" class=\"editorjs-toc__link\">Consecuencia del error\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-84\" class=\"editorjs-toc__link\">Método de diagnóstico \u002F decisión\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-96\" class=\"editorjs-toc__link\">Evidencia\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-104\" class=\"editorjs-toc__link\">Ejemplos reales\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-119\" class=\"editorjs-toc__link\">Conceptos erróneos comunes y modos de fallo\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-125\" class=\"editorjs-toc__link\">Casos límite\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-136\" class=\"editorjs-toc__link\">Limitaciones\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-143\" class=\"editorjs-toc__link\">¿Qué cambiaría esta respuesta?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-149\" class=\"editorjs-toc__link\">Conclusión\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-157\" class=\"editorjs-toc__link\">Fuentes Primarias\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-10\">Qué significa esto realmente\u003C\u002Fh2>\n\u003Cp>Un LLM tiene dos formas fundamentalmente diferentes de obtener información.\u003C\u002Fp>\n\u003Cp>La primera es el conocimiento del modelo. Esta es información representada en los parámetros aprendidos del modelo. No se requiere ninguna consulta a base de datos, búsqueda web o búsqueda de documentos en tiempo de ejecución.\u003C\u002Fp>\n\u003Cp>La segunda es el conocimiento en tiempo de ejecución. Esta es información proporcionada mientras el modelo está operando: resultados de búsqueda, registros de bases de datos, documentos, APIs, archivos de usuario, salidas de herramientas u otra evidencia recuperada.\u003C\u002Fp>\n\u003Cp>RAG conecta estos dos mundos. Pero RAG en sí mismo no responde a la pregunta de cuándo debería activarse esa conexión. Ese es el propósito del Disparador de Recuperación.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Question\n   ↓\nModel Knowledge\n   ↓\nIs internal knowledge sufficient?\n   ↓\nRetrieval Trigger\n   ↓\nExternal Retrieval, if required\n   ↓\nEvidence\n   ↓\nReasoning\n   ↓\nAnswer Validity Boundary\n   ↓\nAnswer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>El Disparador de Recuperación, por lo tanto, se sitúa antes de la recuperación. El Límite de Validez de la Respuesta se sitúa después.\u003C\u002Fp>\n\u003Cp>El primero pregunta: ¿Necesito evidencia externa?\u003C\u002Fp>\n\u003Cp>El segundo pregunta: ¿Tengo ahora suficiente evidencia para respaldar esta respuesta?\u003C\u002Fp>\n\u003Cp>Estas son decisiones relacionadas, pero no son la misma decisión.\u003C\u002Fp>\n\u003Ch2 id=\"section-20\">Ejemplo más simple\u003C\u002Fh2>\n\u003Cp>Considera tres preguntas.\u003C\u002Fp>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Pregunta\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Conocimiento interno\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Disparador de recuperación\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">¿Cuál es la capital de Francia?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Generalmente suficiente\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Sin disparador fuerte\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">¿Cuál es el precio actual de las acciones de NVIDIA?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Potencialmente desactualizado\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Disparar recuperación\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">¿Este nuevo artículo científico demuestra que X causa Y?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">No se puede establecer la afirmación sin examinar la evidencia\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Disparador de recuperación fuerte\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Cp>La primera pregunta se basa en un hecho altamente estable.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>User\n↓\n"What is the capital of France?"\n\nModel knowledge\n↓\nParis\n\nFresh external evidence required?\n↓\nNo\n\nAnswer\n↓\nParis\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Recuperar documentos antes de responder normalmente añadiría poco valor.\u003C\u002Fp>\n\u003Cp>Ahora considera una pregunta cuya respuesta cambia continuamente.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>User\n↓\n"What is the current NVIDIA stock price?"\n\nModel knowledge\n↓\nPotentially outdated\n\nCurrent information required?\n↓\nYes\n\nRETRIEVAL TRIGGER\n↓\nMarket data \u002F search \u002F API\n↓\nAnswer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>El modelo puede saber mucho sobre NVIDIA. Eso no significa que sepa el precio ahora.\u003C\u002Fp>\n\u003Cp>El tercer ejemplo es aún más importante.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>User\n↓\n"Does this new scientific paper prove that X causes Y?"\n\nModel knowledge\n↓\nCan reason about causality,\nstatistics and scientific methodology.\n\nBut:\nthe actual evidence is not available internally.\n\nRETRIEVAL TRIGGER\n↓\nRetrieve the paper\n↓\nInspect methodology\n↓\nInspect results\n↓\nCompare claim with evidence\n↓\nAnswer Validity Boundary\n↓\nAnswer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>La capacidad de razonamiento del modelo puede ser perfectamente útil. El componente que falta es la evidencia.\u003C\u002Fp>\n\u003Cp>Esa distinción es fundamental.\u003C\u002Fp>\n\u003Ch2 id=\"section-33\">Dónde el ejemplo deja de funcionar\u003C\u002Fh2>\n\u003Cp>Los ejemplos anteriores hacen que la decisión parezca binaria: recuperar o no recuperar.\u003C\u002Fp>\n\u003Cp>Los sistemas reales son más complicados. Una pregunta puede contener varias afirmaciones, algunas estables y otras actuales. Los documentos recuperados pueden contradecirse. Un recuperador puede devolver información irrelevante. La información relevante puede existir pero no clasificarse lo suficientemente alto. Un documento puede ser autorizado pero estar desactualizado.\u003C\u002Fp>\n\u003Cp>La recuperación en sí misma también puede introducir contexto incorrecto en una respuesta por lo demás razonable.\u003C\u002Fp>\n\u003Cp>Por esto la recuperación no debe tratarse como un sinónimo automático de verdad.\u003C\u002Fp>\n\u003Cp>La investigación sobre la recuperación adaptativa se ha alejado cada vez más del supuesto de que toda consulta debe recibir la misma estrategia de recuperación.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa>, por ejemplo, explora explícitamente la recuperación bajo demanda en lugar de recuperar indiscriminadamente un número fijo de pasajes para cada entrada. Los autores analizan cómo la recuperación innecesaria o irrelevante puede reducir la calidad de la respuesta.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> selecciona de manera similar entre no recuperación, recuperación de un solo paso y estrategias de recuperación más complejas según la complejidad de la pregunta.\u003C\u002Fp>\n\u003Cp>Así que la pregunta importante no es: ¿Este sistema tiene RAG?\u003C\u002Fp>\n\u003Cp>Es: ¿Puede este sistema reconocer cuándo es necesaria la recuperación y qué tipo de recuperación es apropiada?\u003C\u002Fp>\n\u003Ch2 id=\"section-43\">Respuesta directa\u003C\u002Fh2>\n\u003Cp>Una IA debe activar la recuperación cuando responder requiere información que el conocimiento de su modelo interno no puede proporcionar de forma segura con la frescura, especificidad, procedencia o respaldo probatorio requeridos.\u003C\u002Fp>\n\u003Cp>En sistemas prácticos, un Disparador de Recuperación puede surgir de varias condiciones:\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Need for current information\n        OR\nNeed for exact source-specific information\n        OR\nNeed for evidence or provenance\n        OR\nNeed for private\u002Fuser-specific information\n        OR\nInsufficient knowledge coverage\n        OR\nConflicting evidence\n        OR\nHigh consequence of factual error\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Si ninguna de estas condiciones está presente de manera sustancial, la recuperación puede ser innecesaria. Si una o más están presentes, la evidencia externa pasa a formar parte del proceso de generación de la respuesta.\u003C\u002Fp>\n\u003Ch2 id=\"section-48\">Por qué esto es así\u003C\u002Fh2>\n\u003Cp>El conocimiento interno de un modelo de lenguaje suele describirse como conocimiento paramétrico. Se aprendió durante el entrenamiento y se codificó en los parámetros del modelo.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">El trabajo original de RAG de Lewis et al.\u003C\u002Fa> enmarcó la recuperación como una combinación de esta memoria paramétrica con una memoria externa no paramétrica. La memoria externa puede buscarse y actualizarse sin reentrenar todo el modelo de lenguaje.\u003C\u002Fp>\n\u003Cp>Esta distinción crea un problema de sistemas inevitable.\u003C\u002Fp>\n\u003Cp>El modelo puede saber cosas. Pero el modelo no puede asumir que todo lo que sabe está actualizado, es completo, es lo suficientemente específico y está respaldado por la evidencia requerida.\u003C\u002Fp>\n\u003Cp>Por lo tanto, un modelo puede producir una respuesta lingüísticamente convincente mientras sigue operando más allá del punto en el que su conocimiento interno es suficiente.\u003C\u002Fp>\n\u003Cp>Ese punto es donde un Disparador de Recuperación resulta útil.\u003C\u002Fp>\n\u003Ch2 id=\"section-55\">Contexto\u003C\u002Fh2>\n\u003Cp>El RAG tradicional a menudo se ve así:\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Question\n↓\nRetrieve documents\n↓\nAdd documents to context\n↓\nGenerate answer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Esta arquitectura asume la recuperación antes de la generación. Eso funciona bien para muchas aplicaciones intensivas en conocimiento, pero también puede realizar recuperaciones innecesarias.\u003C\u002Fp>\n\u003Cp>Enfoques más avanzados introducen un paso adaptativo:\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Question\n↓\nEvaluate information requirement\n↓\n        ┌───────────────┐\n        │               │\n   no retrieval      retrieval\n        │               │\n        ↓               ↓\n model knowledge    external evidence\n        │               │\n        └───────┬───────┘\n                ↓\n              answer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> va más allá al considerar la recuperación durante la propia generación. Utiliza la generación próxima y los tokens de baja confianza como señales para recuperar información adicional.\u003C\u002Fp>\n\u003Cp>Self-RAG introduce de manera similar mecanismos que permiten que la recuperación, la generación y la crítica interactúen en lugar de tratar la recuperación como un paso de preprocesamiento incondicional.\u003C\u002Fp>\n\u003Cp>Adaptive-RAG aborda el mismo problema más amplio desde la complejidad de la consulta: diferentes preguntas pueden requerir diferentes estrategias de recuperación.\u003C\u002Fp>\n\u003Cp>Estos enfoques difieren técnicamente. Pero exponen la misma idea arquitectónica: la recuperación debe ser una decisión, no simplemente un interruptor permanente.\u003C\u002Fp>\n\u003Ch2 id=\"section-65\">Supuestos\u003C\u002Fh2>\n\u003Cp>El marco de Retrieval Trigger asume que un sistema tiene acceso a al menos una fuente de información externa cuando se requiere recuperación.\u003C\u002Fp>\n\u003Cp>Esa fuente podría ser búsqueda web, un almacén de documentos, base de datos vectorial, base de datos SQL, grafo de conocimiento, API, sistema empresarial, documento subido por el usuario o salida de herramienta.\u003C\u002Fp>\n\u003Cp>También asume que la recuperación tiene un costo. Ese costo no tiene que ser financiero.\u003C\u002Fp>\n\u003Cp>La recuperación introduce latencia, consumo de tokens, uso de contexto, complejidad de infraestructura y la posibilidad de recuperar información engañosa.\u003C\u002Fp>\n\u003Cp>Por lo tanto, el sistema óptimo no maximiza la recuperación. Maximiza la recuperación apropiada.\u003C\u002Fp>\n\u003Ch2 id=\"section-71\">Variables\u003C\u002Fh2>\n\u003Cp>Un Retrieval Trigger práctico puede considerar cinco variables principales.\u003C\u002Fp>\n\u003Ch3 id=\"section-73\">Frescura\u003C\u002Fh3>\n\u003Cp>¿Qué probabilidad hay de que la información requerida haya cambiado? La capital de Francia tiene muy baja volatilidad. El precio de una acción tiene una volatilidad extremadamente alta.\u003C\u002Fp>\n\u003Ch3 id=\"section-75\">Especificidad\u003C\u002Fh3>\n\u003Cp>¿Requiere la pregunta información de una fuente, documento, organización, cuenta o conjunto de datos en particular? Si el usuario pregunta qué dice un contrato específico, el conocimiento general del modelo es irrelevante. El contrato debe recuperarse.\u003C\u002Fp>\n\u003Ch3 id=\"section-77\">Requisito de evidencia\u003C\u002Fh3>\n\u003Cp>¿Necesita la respuesta procedencia? Un modelo puede saber que una afirmación es generalmente aceptada pero aún así necesitar una fuente cuando la tarea requiere verificación.\u003C\u002Fp>\n\u003Ch3 id=\"section-79\">Cobertura del conocimiento\u003C\u002Fh3>\n\u003Cp>¿Es probable que el tema esté representado adecuadamente en el conocimiento interno del modelo? La información rara, propietaria, altamente local o recién publicada crea una mayor presión de recuperación.\u003C\u002Fp>\n\u003Ch3 id=\"section-81\">Consecuencia del error\u003C\u002Fh3>\n\u003Cp>No todas las respuestas incorrectas tienen el mismo impacto. Cuando la precisión fáctica afecta materialmente a una decisión, el umbral de evidencia aceptable puede ser más alto.\u003C\u002Fp>\n\u003Cp>Estas variables no tienen que implementarse como puntuaciones numéricas literales. Describen la superficie de decisión.\u003C\u002Fp>\n\u003Ch2 id=\"section-84\">Método de diagnóstico \u002F decisión\u003C\u002Fh2>\n\u003Cp>Se puede implementar un Disparador de Recuperación muy simple sin aprendizaje automático.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>def should_retrieve(\n    time_sensitive=False,\n    source_specific=False,\n    evidence_required=False,\n    private_context=False,\n    knowledge_uncertain=False,\n    conflicting_information=False\n):\n    return any([\n        time_sensitive,\n        source_specific,\n        evidence_required,\n        private_context,\n        knowledge_uncertain,\n        conflicting_information,\n    ])\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Para una pregunta fáctica estable:\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>should_retrieve()\n# False\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Para un precio de acción actual:\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>should_retrieve(\n    time_sensitive=True\n)\n# True\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Para una afirmación científica:\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>should_retrieve(\n    source_specific=True,\n    evidence_required=True\n)\n# True\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Los sistemas de producción pueden hacer esta decisión mucho más sofisticada. Un clasificador podría predecir los requisitos de recuperación. Un modelo podría emitir tokens de control especiales. Un enrutador podría clasificar la complejidad de la consulta. La recuperación también podría activarse repetidamente durante la generación.\u003C\u002Fp>\n\u003Cp>La implementación puede cambiar. La cuestión arquitectónica sigue siendo la misma:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">¿Es la evidencia actualmente disponible para el modelo suficiente para la respuesta que está a punto de producir?\u003C\u002Fblockquote>\n\u003Ch2 id=\"section-96\">Evidencia\u003C\u002Fh2>\n\u003Cp>El concepto propuesto aquí es consistente con varias líneas de investigación sobre recuperación.\u003C\u002Fp>\n\u003Cp>La \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">arquitectura RAG\u003C\u002Fa> original demostró la utilidad de combinar el conocimiento paramétrico del modelo con conocimiento externo no paramétrico, particularmente para tareas intensivas en conocimiento.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> explora explícitamente la recuperación activa durante la generación, incluida la recuperación provocada por contenido próximo de baja confianza.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa> demuestra una arquitectura en la que la recuperación puede ocurrir bajo demanda y va seguida de una reflexión sobre los pasajes recuperados y el contenido generado.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> elige dinámicamente entre diferentes estrategias según la complejidad de la pregunta, incluidas situaciones en las que no se requiere recuperación.\u003C\u002Fp>\n\u003Cp>El término Disparador de Recuperación se utiliza aquí como una abstracción a nivel de sistema sobre esta familia más amplia de decisiones.\u003C\u002Fp>\n\u003Cp>No afirma que estos artículos utilicen la misma terminología. En cambio, identifica el problema arquitectónico compartido: ¿Qué hace que un sistema de IA pase del conocimiento interno a la evidencia externa?\u003C\u002Fp>\n\u003Ch2 id=\"section-104\">Ejemplos reales\u003C\u002Fh2>\n\u003Cp>Considere un asistente de soporte conectado a la documentación de una empresa.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"How do I reset my password?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Si el procedimiento es estable y está representado de manera fiable en las instrucciones actuales del asistente, la respuesta directa puede ser apropiada.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"What permissions does my account currently have?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Esa información es específica del usuario y dinámica. Se activa el Disparador de Recuperación. El sistema debe inspeccionar los datos reales de la cuenta o de autorización.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"Why was my production deployment rejected yesterday?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>El modelo puede entender los sistemas de despliegue y explicar razones comunes. Pero la pregunta se refiere a un evento particular. Se requieren registros, salida de CI\u002FCD o registros de incidentes.\u003C\u002Fp>\n\u003Cp>La misma lógica se aplica a la búsqueda web.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"What is RAG?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Una explicación general puede no requerir recuperación.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"What did the authors of Self-RAG specifically conclude about unnecessary retrieval?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Ahora se requiere evidencia específica de la fuente.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"What is the latest research on adaptive retrieval?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Esto también introduce un requisito de actualidad. El tema subyacente no ha cambiado. El requisito de información sí.\u003C\u002Fp>\n\u003Ch2 id=\"section-119\">Conceptos erróneos comunes y modos de fallo\u003C\u002Fh2>\n\u003Cp>Más recuperación produce automáticamente una mejor respuesta. No es así. Los documentos irrelevantes consumen contexto y pueden distraer la generación.\u003C\u002Fp>\n\u003Cp>Una alta confianza del modelo significa que la recuperación es innecesaria. Un modelo puede producir una respuesta incorrecta con confianza. Por lo tanto, la confianza autoinformada no debe tratarse como el único disparador.\u003C\u002Fp>\n\u003Cp>Una recuperación exitosa significa que la respuesta está verificada. La recuperación solo proporciona evidencia candidata. La evidencia aún debe ser relevante, suficientemente autorizada e interpretada correctamente.\u003C\u002Fp>\n\u003Cp>RAG resuelve automáticamente el conocimiento desactualizado. Solo lo hace si el propio corpus de recuperación contiene información actual. Recuperar un documento desactualizado no crea una respuesta actual.\u003C\u002Fp>\n\u003Cp>Un paso de recuperación siempre es suficiente. Las preguntas complejas pueden requerir varias piezas de evidencia o recuperación iterativa.\u003C\u002Fp>\n\u003Ch2 id=\"section-125\">Casos límite\u003C\u002Fh2>\n\u003Cp>Algunas preguntas contienen información tanto estable como inestable.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"Who founded NVIDIA, and what is its market capitalization today?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>La primera parte puede ser respondible a partir del conocimiento estable del modelo. La segunda parte requiere información actual.\u003C\u002Fp>\n\u003Cp>Un sistema suficientemente capaz no debería tratar necesariamente toda la consulta como una única decisión de recuperación. Puede activar la recuperación solo donde sea necesario.\u003C\u002Fp>\n\u003Cp>Otro caso límite es el desacuerdo entre fuentes. Supongamos que la recuperación devuelve tres documentos que hacen afirmaciones incompatibles.\u003C\u002Fp>\n\u003Cp>El Disparador de Recuperación ya ha tenido éxito: el sistema reconoció que se requería evidencia externa. Pero la tarea no ha terminado.\u003C\u002Fp>\n\u003Cp>El sistema ha alcanzado ahora un problema de evaluación de evidencia. Aquí es donde el Límite de Validez de la Respuesta se vuelve importante.\u003C\u002Fp>\n\u003Cp>El sistema puede haber recuperado información y aún no poseer evidencia suficiente para llegar a una conclusión sólida.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Retrieval Trigger\n≠\npermission to answer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>El disparador obtiene evidencia. El límite de validez determina si esa evidencia es suficiente.\u003C\u002Fp>\n\u003Ch2 id=\"section-136\">Limitaciones\u003C\u002Fh2>\n\u003Cp>El Disparador de Recuperación es un marco conceptual, no un algoritmo universal.\u003C\u002Fp>\n\u003Cp>Diferentes sistemas requerirán diferentes reglas de activación. Un bot de atención al cliente, un asistente de investigación científica, un motor de búsqueda y un agente de software autónomo no tienen requisitos de evidencia idénticos.\u003C\u002Fp>\n\u003Cp>Los umbrales de activación también pueden crear sus propios modos de fallo. Un umbral demasiado bajo causa una recuperación excesiva. Un umbral demasiado alto causa respuestas sin respaldo.\u003C\u002Fp>\n\u003Cp>La infraestructura de recuperación en sí también importa. Un disparador perfecto conectado a una colección de fuentes pobre sigue produciendo evidencia pobre.\u003C\u002Fp>\n\u003Cp>De manera similar, una base de conocimiento excelente proporciona poco valor si el disparador nunca se activa cuando se necesita.\u003C\u002Fp>\n\u003Cp>El Disparador de Recuperación, por lo tanto, resuelve solo una parte de una arquitectura más amplia.\u003C\u002Fp>\n\u003Ch2 id=\"section-143\">¿Qué cambiaría esta respuesta?\u003C\u002Fh2>\n\u003Cp>Los modelos futuros pueden contener mejores mecanismos para identificar sus propias limitaciones de conocimiento. Los recuperadores pueden volverse más baratos y rápidos. Los sistemas de contexto largo pueden llevar mucho más material fuente de forma continua.\u003C\u002Fp>\n\u003Cp>Los modelos también pueden combinar cada vez más búsqueda, bases de datos, herramientas y conocimiento estructurado sin exponer una etapa RAG distinta al desarrollador de la aplicación.\u003C\u002Fp>\n\u003Cp>Estos cambios podrían alterar cómo se implementa el disparador. No necesariamente eliminan la decisión subyacente.\u003C\u002Fp>\n\u003Cp>Siempre que exista una diferencia entre la información ya disponible para el modelo y la información que debe obtenerse externamente, un sistema aún necesita algún mecanismo para determinar cuándo cruzar esa frontera.\u003C\u002Fp>\n\u003Cp>La implementación puede desaparecer de la vista. La cuestión arquitectónica permanece.\u003C\u002Fp>\n\u003Ch2 id=\"section-149\">Conclusión\u003C\u002Fh2>\n\u003Cp>RAG comienza demasiado tarde para explicar todo el problema.\u003C\u002Fp>\n\u003Cp>Antes de que pueda ocurrir la recuperación, un sistema de IA debe determinar si la recuperación es necesaria. Esa decisión es el Disparador de Recuperación.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Stable known fact\n→ answer from model knowledge\n\nCurrent fact\n→ retrieve\n\nSource-specific or evidence-dependent claim\n→ retrieve and verify\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Pero la implicación más amplia es más importante. La IA confiable no solo necesita acceso al conocimiento. Necesita un método para determinar cuándo su conocimiento actual es insuficiente.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Model Knowledge\n        ↓\nRetrieval Trigger\n        ↓\nRuntime Knowledge \u002F RAG\n        ↓\nEvidence\n        ↓\nReasoning\n        ↓\nAnswer Validity Boundary\n        ↓\nAnswer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>El Disparador de Recuperación determina cuándo el sistema debe buscar evidencia. El Límite de Validez de la Respuesta determina si esa evidencia es suficiente.\u003C\u002Fp>\n\u003Cp>Juntos describen algo más útil que RAG por sí solo: un proceso de decisión para pasar de lo que una IA parece saber hacia lo que realmente puede respaldar.\u003C\u002Fp>\n\u003Ch2 id=\"section-157\">Fuentes Primarias\u003C\u002Fh2>\n\u003Cp>Patrick Lewis et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\u003C\u002Fa> (2020). Trabajo fundacional de RAG que describe la combinación de memoria paramétrica del modelo con memoria no paramétrica externa.\u003C\u002Fp>\n\u003Cp>Zhengbao Jiang et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">Active Retrieval Augmented Generation\u003C\u002Fa> (2023). Introduce FLARE y la recuperación activa durante la generación, incluida la recuperación basada en contenido predicho con baja confianza.\u003C\u002Fp>\n\u003Cp>Akari Asai et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection\u003C\u002Fa> (2023). Explora la recuperación adaptativa bajo demanda y la autorreflexión en lugar de la recuperación fija incondicional.\u003C\u002Fp>\n\u003Cp>Soyeong Jeong et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity\u003C\u002Fa> (2024). Selecciona dinámicamente entre sin recuperación, recuperación de un solo paso y estrategias de recuperación más complejas según la pregunta entrante.\u003C\u002Fp>",{"time":212,"blocks":213,"version":1044},1790575151712,[214,220,226,231,236,241,246,251,259,266,271,276,281,286,291,297,302,307,312,317,322,327,348,353,358,363,368,373,378,383,388,393,398,403,408,413,418,423,428,433,438,443,448,453,458,463,468,473,478,483,488,493,498,503,508,513,518,523,528,533,538,543,548,553,558,563,568,573,578,583,588,593,598,603,608,613,618,623,628,633,638,643,648,653,658,663,668,673,678,683,688,693,698,703,708,714,719,724,729,734,739,744,749,754,759,764,769,774,779,784,789,794,799,804,809,814,819,824,829,834,839,844,849,854,859,864,869,874,879,884,889,894,899,904,909,914,919,924,929,934,939,944,949,954,959,964,969,974,979,984,989,994,999,1004,1009,1014,1019,1024,1029,1034,1039],{"id":215,"data":216,"type":42,"tunes":219},"Wt7UfNeFlS",{"text":217,"level":218},"Pregunta",2,{},{"id":221,"data":222,"type":224,"tunes":225},"T-ZCQblBzm",{"text":223},"¿Cuándo debería una IA dejar de confiar en lo que ya sabe y recuperar información externa antes de responder?","paragraph",{},{"id":227,"data":228,"type":224,"tunes":230},"vBcd4061WS",{"text":229},"Esta pregunta parece simple, pero se sitúa en el centro de una de las decisiones de diseño más importantes en los sistemas de IA modernos.",{},{"id":232,"data":233,"type":224,"tunes":235},"r9NZ-Fzw0e",{"text":234},"Los grandes modelos de lenguaje contienen un conocimiento sustancial en sus parámetros. La Generación Aumentada por Recuperación añade información externa en tiempo de ejecución. Pero ninguno de los extremos es ideal.",{},{"id":237,"data":238,"type":224,"tunes":240},"CpzlgJjAVL",{"text":239},"Confiar siempre en el modelo puede producir respuestas desactualizadas o sin respaldo. Recuperar información siempre añade latencia, costo, contexto irrelevante y nuevas oportunidades para errores de recuperación.",{},{"id":242,"data":243,"type":224,"tunes":245},"yeclJhYJ1a",{"text":244},"El verdadero problema, por lo tanto, viene antes de RAG: ¿Cuándo debería ocurrir la recuperación en absoluto?",{},{"id":247,"data":248,"type":224,"tunes":250},"FgLSWvpZMg",{"text":249},"Este artículo utiliza el término Disparador de Recuperación para esa decisión. El Disparador de Recuperación no se presenta aquí como un término estandarizado de la literatura de investigación. Es un concepto práctico de sistemas que reúne ideas ya visibles en la investigación sobre recuperación activa, adaptativa y autorreflexiva.",{},{"id":252,"data":253,"type":257,"tunes":258},"Muzvv-2uzU",{"text":254,"caption":255,"alignment":256},"Un Disparador de Recuperación es una condición que indica que un sistema de IA debería dejar de confiar únicamente en el conocimiento interno del modelo y obtener evidencia externa antes de producir o finalizar una respuesta.","Definición de trabajo","left","quote",{},{"id":260,"data":261,"type":264,"tunes":265},"1BGt1waZ01",{"title":262,"maxLevel":263,"minLevel":218},"Contenido",3,"tableOfContents",{},{"id":267,"data":268,"type":42,"tunes":270},"BFKJ2htjYN",{"text":269,"level":218},"Qué significa esto realmente",{},{"id":272,"data":273,"type":224,"tunes":275},"yfBYqVwObv",{"text":274},"Un LLM tiene dos formas fundamentalmente diferentes de obtener información.",{},{"id":277,"data":278,"type":224,"tunes":280},"Y4JYebztDi",{"text":279},"La primera es el conocimiento del modelo. Esta es información representada en los parámetros aprendidos del modelo. No se requiere ninguna consulta a base de datos, búsqueda web o búsqueda de documentos en tiempo de ejecución.",{},{"id":282,"data":283,"type":224,"tunes":285},"x2L37FSTBK",{"text":284},"La segunda es el conocimiento en tiempo de ejecución. Esta es información proporcionada mientras el modelo está operando: resultados de búsqueda, registros de bases de datos, documentos, APIs, archivos de usuario, salidas de herramientas u otra evidencia recuperada.",{},{"id":287,"data":288,"type":224,"tunes":290},"2szDUb7_-4",{"text":289},"RAG conecta estos dos mundos. Pero RAG en sí mismo no responde a la pregunta de cuándo debería activarse esa conexión. Ese es el propósito del Disparador de Recuperación.",{},{"id":292,"data":293,"type":295,"tunes":296},"5_yjTthHV4",{"code":294},"Question\n   ↓\nModel Knowledge\n   ↓\nIs internal knowledge sufficient?\n   ↓\nRetrieval Trigger\n   ↓\nExternal Retrieval, if required\n   ↓\nEvidence\n   ↓\nReasoning\n   ↓\nAnswer Validity Boundary\n   ↓\nAnswer","code",{},{"id":298,"data":299,"type":224,"tunes":301},"rH2K36ambR",{"text":300},"El Disparador de Recuperación, por lo tanto, se sitúa antes de la recuperación. El Límite de Validez de la Respuesta se sitúa después.",{},{"id":303,"data":304,"type":224,"tunes":306},"L0WlGs_dTF",{"text":305},"El primero pregunta: ¿Necesito evidencia externa?",{},{"id":308,"data":309,"type":224,"tunes":311},"JyE4O9aDCW",{"text":310},"El segundo pregunta: ¿Tengo ahora suficiente evidencia para respaldar esta respuesta?",{},{"id":313,"data":314,"type":224,"tunes":316},"L9JP5xByy4",{"text":315},"Estas son decisiones relacionadas, pero no son la misma decisión.",{},{"id":318,"data":319,"type":42,"tunes":321},"4hPbiDSHek",{"text":320,"level":218},"Ejemplo más simple",{},{"id":323,"data":324,"type":224,"tunes":326},"cER32Me6gA",{"text":325},"Considera tres preguntas.",{},{"id":328,"data":329,"type":346,"tunes":347},"izi7nU9FE9",{"content":330,"stretched":43,"withHeadings":14},[331,334,338,342],[217,332,333],"Conocimiento interno","Disparador de recuperación",[335,336,337],"¿Cuál es la capital de Francia?","Generalmente suficiente","Sin disparador fuerte",[339,340,341],"¿Cuál es el precio actual de las acciones de NVIDIA?","Potencialmente desactualizado","Disparar recuperación",[343,344,345],"¿Este nuevo artículo científico demuestra que X causa Y?","No se puede establecer la afirmación sin examinar la evidencia","Disparador de recuperación fuerte","table",{},{"id":349,"data":350,"type":224,"tunes":352},"cb-Kx0fKs4",{"text":351},"La primera pregunta se basa en un hecho altamente estable.",{},{"id":354,"data":355,"type":295,"tunes":357},"MZJzwvZUH7",{"code":356},"User\n↓\n\"What is the capital of France?\"\n\nModel knowledge\n↓\nParis\n\nFresh external evidence required?\n↓\nNo\n\nAnswer\n↓\nParis",{},{"id":359,"data":360,"type":224,"tunes":362},"fRP7-aWTJB",{"text":361},"Recuperar documentos antes de responder normalmente añadiría poco valor.",{},{"id":364,"data":365,"type":224,"tunes":367},"O2TaSvLoxO",{"text":366},"Ahora considera una pregunta cuya respuesta cambia continuamente.",{},{"id":369,"data":370,"type":295,"tunes":372},"cNv0Dp7Mk3",{"code":371},"User\n↓\n\"What is the current NVIDIA stock price?\"\n\nModel knowledge\n↓\nPotentially outdated\n\nCurrent information required?\n↓\nYes\n\nRETRIEVAL TRIGGER\n↓\nMarket data \u002F search \u002F API\n↓\nAnswer",{},{"id":374,"data":375,"type":224,"tunes":377},"Y3NDw8awnA",{"text":376},"El modelo puede saber mucho sobre NVIDIA. Eso no significa que sepa el precio ahora.",{},{"id":379,"data":380,"type":224,"tunes":382},"FwjiaA6mdJ",{"text":381},"El tercer ejemplo es aún más importante.",{},{"id":384,"data":385,"type":295,"tunes":387},"G48ZGtX4XK",{"code":386},"User\n↓\n\"Does this new scientific paper prove that X causes Y?\"\n\nModel knowledge\n↓\nCan reason about causality,\nstatistics and scientific methodology.\n\nBut:\nthe actual evidence is not available internally.\n\nRETRIEVAL TRIGGER\n↓\nRetrieve the paper\n↓\nInspect methodology\n↓\nInspect results\n↓\nCompare claim with evidence\n↓\nAnswer Validity Boundary\n↓\nAnswer",{},{"id":389,"data":390,"type":224,"tunes":392},"nGu-KcQC6l",{"text":391},"La capacidad de razonamiento del modelo puede ser perfectamente útil. El componente que falta es la evidencia.",{},{"id":394,"data":395,"type":224,"tunes":397},"_bUxnOYvHG",{"text":396},"Esa distinción es fundamental.",{},{"id":399,"data":400,"type":42,"tunes":402},"etbE_esRx4",{"text":401,"level":218},"Dónde el ejemplo deja de funcionar",{},{"id":404,"data":405,"type":224,"tunes":407},"0iSdy2Msw7",{"text":406},"Los ejemplos anteriores hacen que la decisión parezca binaria: recuperar o no recuperar.",{},{"id":409,"data":410,"type":224,"tunes":412},"8Go7nm2niJ",{"text":411},"Los sistemas reales son más complicados. Una pregunta puede contener varias afirmaciones, algunas estables y otras actuales. Los documentos recuperados pueden contradecirse. Un recuperador puede devolver información irrelevante. La información relevante puede existir pero no clasificarse lo suficientemente alto. Un documento puede ser autorizado pero estar desactualizado.",{},{"id":414,"data":415,"type":224,"tunes":417},"8dVjRU5cXg",{"text":416},"La recuperación en sí misma también puede introducir contexto incorrecto en una respuesta por lo demás razonable.",{},{"id":419,"data":420,"type":224,"tunes":422},"pLqSH5-OJR",{"text":421},"Por esto la recuperación no debe tratarse como un sinónimo automático de verdad.",{},{"id":424,"data":425,"type":224,"tunes":427},"ww4Od2cmTr",{"text":426},"La investigación sobre la recuperación adaptativa se ha alejado cada vez más del supuesto de que toda consulta debe recibir la misma estrategia de recuperación.",{},{"id":429,"data":430,"type":224,"tunes":432},"1yE2LUP7cF",{"text":431},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa>, por ejemplo, explora explícitamente la recuperación bajo demanda en lugar de recuperar indiscriminadamente un número fijo de pasajes para cada entrada. Los autores analizan cómo la recuperación innecesaria o irrelevante puede reducir la calidad de la respuesta.",{},{"id":434,"data":435,"type":224,"tunes":437},"915QBDW89m",{"text":436},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> selecciona de manera similar entre no recuperación, recuperación de un solo paso y estrategias de recuperación más complejas según la complejidad de la pregunta.",{},{"id":439,"data":440,"type":224,"tunes":442},"1FBgxY0QQp",{"text":441},"Así que la pregunta importante no es: ¿Este sistema tiene RAG?",{},{"id":444,"data":445,"type":224,"tunes":447},"4xdj86u8Qz",{"text":446},"Es: ¿Puede este sistema reconocer cuándo es necesaria la recuperación y qué tipo de recuperación es apropiada?",{},{"id":449,"data":450,"type":42,"tunes":452},"bGPa0AsJI6",{"text":451,"level":218},"Respuesta directa",{},{"id":454,"data":455,"type":224,"tunes":457},"fBKcyJ0IcX",{"text":456},"Una IA debe activar la recuperación cuando responder requiere información que el conocimiento de su modelo interno no puede proporcionar de forma segura con la frescura, especificidad, procedencia o respaldo probatorio requeridos.",{},{"id":459,"data":460,"type":224,"tunes":462},"JbIPIJjkXK",{"text":461},"En sistemas prácticos, un Disparador de Recuperación puede surgir de varias condiciones:",{},{"id":464,"data":465,"type":295,"tunes":467},"_JbTSHlrtH",{"code":466},"Need for current information\n        OR\nNeed for exact source-specific information\n        OR\nNeed for evidence or provenance\n        OR\nNeed for private\u002Fuser-specific information\n        OR\nInsufficient knowledge coverage\n        OR\nConflicting evidence\n        OR\nHigh consequence of factual error",{},{"id":469,"data":470,"type":224,"tunes":472},"Aaem6fQ_tF",{"text":471},"Si ninguna de estas condiciones está presente de manera sustancial, la recuperación puede ser innecesaria. Si una o más están presentes, la evidencia externa pasa a formar parte del proceso de generación de la respuesta.",{},{"id":474,"data":475,"type":42,"tunes":477},"x2DDg7Ue1-",{"text":476,"level":218},"Por qué esto es así",{},{"id":479,"data":480,"type":224,"tunes":482},"1GTaWG9ViB",{"text":481},"El conocimiento interno de un modelo de lenguaje suele describirse como conocimiento paramétrico. Se aprendió durante el entrenamiento y se codificó en los parámetros del modelo.",{},{"id":484,"data":485,"type":224,"tunes":487},"klwNY3lr1d",{"text":486},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">El trabajo original de RAG de Lewis et al.\u003C\u002Fa> enmarcó la recuperación como una combinación de esta memoria paramétrica con una memoria externa no paramétrica. La memoria externa puede buscarse y actualizarse sin reentrenar todo el modelo de lenguaje.",{},{"id":489,"data":490,"type":224,"tunes":492},"Fyw2AVDbxR",{"text":491},"Esta distinción crea un problema de sistemas inevitable.",{},{"id":494,"data":495,"type":224,"tunes":497},"4FvbthV3in",{"text":496},"El modelo puede saber cosas. Pero el modelo no puede asumir que todo lo que sabe está actualizado, es completo, es lo suficientemente específico y está respaldado por la evidencia requerida.",{},{"id":499,"data":500,"type":224,"tunes":502},"asxdihTbcB",{"text":501},"Por lo tanto, un modelo puede producir una respuesta lingüísticamente convincente mientras sigue operando más allá del punto en el que su conocimiento interno es suficiente.",{},{"id":504,"data":505,"type":224,"tunes":507},"jGgq116uAa",{"text":506},"Ese punto es donde un Disparador de Recuperación resulta útil.",{},{"id":509,"data":510,"type":42,"tunes":512},"T6q_BUeDg3",{"text":511,"level":218},"Contexto",{},{"id":514,"data":515,"type":224,"tunes":517},"9H_bNlyoYs",{"text":516},"El RAG tradicional a menudo se ve así:",{},{"id":519,"data":520,"type":295,"tunes":522},"YSZR1AInSj",{"code":521},"Question\n↓\nRetrieve documents\n↓\nAdd documents to context\n↓\nGenerate answer",{},{"id":524,"data":525,"type":224,"tunes":527},"HnzY2Q9xTs",{"text":526},"Esta arquitectura asume la recuperación antes de la generación. Eso funciona bien para muchas aplicaciones intensivas en conocimiento, pero también puede realizar recuperaciones innecesarias.",{},{"id":529,"data":530,"type":224,"tunes":532},"Aho03YTGAU",{"text":531},"Enfoques más avanzados introducen un paso adaptativo:",{},{"id":534,"data":535,"type":295,"tunes":537},"uK0l0tYLg6",{"code":536},"Question\n↓\nEvaluate information requirement\n↓\n        ┌───────────────┐\n        │               │\n   no retrieval      retrieval\n        │               │\n        ↓               ↓\n model knowledge    external evidence\n        │               │\n        └───────┬───────┘\n                ↓\n              answer",{},{"id":539,"data":540,"type":224,"tunes":542},"Upb-15aN8T",{"text":541},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> va más allá al considerar la recuperación durante la propia generación. Utiliza la generación próxima y los tokens de baja confianza como señales para recuperar información adicional.",{},{"id":544,"data":545,"type":224,"tunes":547},"I5hs5j9IKc",{"text":546},"Self-RAG introduce de manera similar mecanismos que permiten que la recuperación, la generación y la crítica interactúen en lugar de tratar la recuperación como un paso de preprocesamiento incondicional.",{},{"id":549,"data":550,"type":224,"tunes":552},"mw2jbuWA-g",{"text":551},"Adaptive-RAG aborda el mismo problema más amplio desde la complejidad de la consulta: diferentes preguntas pueden requerir diferentes estrategias de recuperación.",{},{"id":554,"data":555,"type":224,"tunes":557},"DUba0EfbWg",{"text":556},"Estos enfoques difieren técnicamente. Pero exponen la misma idea arquitectónica: la recuperación debe ser una decisión, no simplemente un interruptor permanente.",{},{"id":559,"data":560,"type":42,"tunes":562},"wzX0jC8H8b",{"text":561,"level":218},"Supuestos",{},{"id":564,"data":565,"type":224,"tunes":567},"4puAk8h-NF",{"text":566},"El marco de Retrieval Trigger asume que un sistema tiene acceso a al menos una fuente de información externa cuando se requiere recuperación.",{},{"id":569,"data":570,"type":224,"tunes":572},"Qsm42lc7aC",{"text":571},"Esa fuente podría ser búsqueda web, un almacén de documentos, base de datos vectorial, base de datos SQL, grafo de conocimiento, API, sistema empresarial, documento subido por el usuario o salida de herramienta.",{},{"id":574,"data":575,"type":224,"tunes":577},"Rznt7yvqT2",{"text":576},"También asume que la recuperación tiene un costo. Ese costo no tiene que ser financiero.",{},{"id":579,"data":580,"type":224,"tunes":582},"wQoEfZuFPe",{"text":581},"La recuperación introduce latencia, consumo de tokens, uso de contexto, complejidad de infraestructura y la posibilidad de recuperar información engañosa.",{},{"id":584,"data":585,"type":224,"tunes":587},"WM1F9QkT2G",{"text":586},"Por lo tanto, el sistema óptimo no maximiza la recuperación. Maximiza la recuperación apropiada.",{},{"id":589,"data":590,"type":42,"tunes":592},"Z4gw9SX7jo",{"text":591,"level":218},"Variables",{},{"id":594,"data":595,"type":224,"tunes":597},"Z_sKNO6vmp",{"text":596},"Un Retrieval Trigger práctico puede considerar cinco variables principales.",{},{"id":599,"data":600,"type":42,"tunes":602},"Eti88tz1T6",{"text":601,"level":263},"Frescura",{},{"id":604,"data":605,"type":224,"tunes":607},"3zKe198lls",{"text":606},"¿Qué probabilidad hay de que la información requerida haya cambiado? La capital de Francia tiene muy baja volatilidad. El precio de una acción tiene una volatilidad extremadamente alta.",{},{"id":609,"data":610,"type":42,"tunes":612},"ryQRR7TzC7",{"text":611,"level":263},"Especificidad",{},{"id":614,"data":615,"type":224,"tunes":617},"bkXBBuCBb_",{"text":616},"¿Requiere la pregunta información de una fuente, documento, organización, cuenta o conjunto de datos en particular? Si el usuario pregunta qué dice un contrato específico, el conocimiento general del modelo es irrelevante. El contrato debe recuperarse.",{},{"id":619,"data":620,"type":42,"tunes":622},"LlT6c-tPU2",{"text":621,"level":263},"Requisito de evidencia",{},{"id":624,"data":625,"type":224,"tunes":627},"1G-aWjGT1c",{"text":626},"¿Necesita la respuesta procedencia? Un modelo puede saber que una afirmación es generalmente aceptada pero aún así necesitar una fuente cuando la tarea requiere verificación.",{},{"id":629,"data":630,"type":42,"tunes":632},"lnoOCm4KDw",{"text":631,"level":263},"Cobertura del conocimiento",{},{"id":634,"data":635,"type":224,"tunes":637},"nKGrZO0Zw0",{"text":636},"¿Es probable que el tema esté representado adecuadamente en el conocimiento interno del modelo? La información rara, propietaria, altamente local o recién publicada crea una mayor presión de recuperación.",{},{"id":639,"data":640,"type":42,"tunes":642},"SYp_4G0qXz",{"text":641,"level":263},"Consecuencia del error",{},{"id":644,"data":645,"type":224,"tunes":647},"YJeo8nKsl9",{"text":646},"No todas las respuestas incorrectas tienen el mismo impacto. Cuando la precisión fáctica afecta materialmente a una decisión, el umbral de evidencia aceptable puede ser más alto.",{},{"id":649,"data":650,"type":224,"tunes":652},"NTh27HJjo1",{"text":651},"Estas variables no tienen que implementarse como puntuaciones numéricas literales. Describen la superficie de decisión.",{},{"id":654,"data":655,"type":42,"tunes":657},"A25id0cm1s",{"text":656,"level":218},"Método de diagnóstico \u002F decisión",{},{"id":659,"data":660,"type":224,"tunes":662},"az70f7cIIF",{"text":661},"Se puede implementar un Disparador de Recuperación muy simple sin aprendizaje automático.",{},{"id":664,"data":665,"type":295,"tunes":667},"yUFgVx9VRM",{"code":666},"def should_retrieve(\n    time_sensitive=False,\n    source_specific=False,\n    evidence_required=False,\n    private_context=False,\n    knowledge_uncertain=False,\n    conflicting_information=False\n):\n    return any([\n        time_sensitive,\n        source_specific,\n        evidence_required,\n        private_context,\n        knowledge_uncertain,\n        conflicting_information,\n    ])",{},{"id":669,"data":670,"type":224,"tunes":672},"tBn6sOGnKB",{"text":671},"Para una pregunta fáctica estable:",{},{"id":674,"data":675,"type":295,"tunes":677},"BW2rsTbqqL",{"code":676},"should_retrieve()\n# False",{},{"id":679,"data":680,"type":224,"tunes":682},"iriE0iq97f",{"text":681},"Para un precio de acción actual:",{},{"id":684,"data":685,"type":295,"tunes":687},"d10aolm-TW",{"code":686},"should_retrieve(\n    time_sensitive=True\n)\n# True",{},{"id":689,"data":690,"type":224,"tunes":692},"nwL_vpUi-Y",{"text":691},"Para una afirmación científica:",{},{"id":694,"data":695,"type":295,"tunes":697},"-DXgs4BBKH",{"code":696},"should_retrieve(\n    source_specific=True,\n    evidence_required=True\n)\n# True",{},{"id":699,"data":700,"type":224,"tunes":702},"Wj1sAbZ7l8",{"text":701},"Los sistemas de producción pueden hacer esta decisión mucho más sofisticada. Un clasificador podría predecir los requisitos de recuperación. Un modelo podría emitir tokens de control especiales. Un enrutador podría clasificar la complejidad de la consulta. La recuperación también podría activarse repetidamente durante la generación.",{},{"id":704,"data":705,"type":224,"tunes":707},"loLbe4TkAK",{"text":706},"La implementación puede cambiar. La cuestión arquitectónica sigue siendo la misma:",{},{"id":709,"data":710,"type":257,"tunes":713},"T2PJWaSYp9",{"text":711,"caption":712,"alignment":256},"¿Es la evidencia actualmente disponible para el modelo suficiente para la respuesta que está a punto de producir?","",{},{"id":715,"data":716,"type":42,"tunes":718},"9Alw1zzG4E",{"text":717,"level":218},"Evidencia",{},{"id":720,"data":721,"type":224,"tunes":723},"OgqdUwG1J-",{"text":722},"El concepto propuesto aquí es consistente con varias líneas de investigación sobre recuperación.",{},{"id":725,"data":726,"type":224,"tunes":728},"QxIkEDnlBu",{"text":727},"La \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">arquitectura RAG\u003C\u002Fa> original demostró la utilidad de combinar el conocimiento paramétrico del modelo con conocimiento externo no paramétrico, particularmente para tareas intensivas en conocimiento.",{},{"id":730,"data":731,"type":224,"tunes":733},"nqdi_kDLE3",{"text":732},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> explora explícitamente la recuperación activa durante la generación, incluida la recuperación provocada por contenido próximo de baja confianza.",{},{"id":735,"data":736,"type":224,"tunes":738},"UThAFgyEe3",{"text":737},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa> demuestra una arquitectura en la que la recuperación puede ocurrir bajo demanda y va seguida de una reflexión sobre los pasajes recuperados y el contenido generado.",{},{"id":740,"data":741,"type":224,"tunes":743},"CT8n5F5KLR",{"text":742},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> elige dinámicamente entre diferentes estrategias según la complejidad de la pregunta, incluidas situaciones en las que no se requiere recuperación.",{},{"id":745,"data":746,"type":224,"tunes":748},"RDjo1UGf2s",{"text":747},"El término Disparador de Recuperación se utiliza aquí como una abstracción a nivel de sistema sobre esta familia más amplia de decisiones.",{},{"id":750,"data":751,"type":224,"tunes":753},"nmWZ-exi8b",{"text":752},"No afirma que estos artículos utilicen la misma terminología. En cambio, identifica el problema arquitectónico compartido: ¿Qué hace que un sistema de IA pase del conocimiento interno a la evidencia externa?",{},{"id":755,"data":756,"type":42,"tunes":758},"8a-H_OlfG1",{"text":757,"level":218},"Ejemplos reales",{},{"id":760,"data":761,"type":224,"tunes":763},"l0KONt5Buo",{"text":762},"Considere un asistente de soporte conectado a la documentación de una empresa.",{},{"id":765,"data":766,"type":295,"tunes":768},"MOy11BOFq5",{"code":767},"\"How do I reset my password?\"",{},{"id":770,"data":771,"type":224,"tunes":773},"tsZcuMS1sT",{"text":772},"Si el procedimiento es estable y está representado de manera fiable en las instrucciones actuales del asistente, la respuesta directa puede ser apropiada.",{},{"id":775,"data":776,"type":295,"tunes":778},"l53NPB6WNV",{"code":777},"\"What permissions does my account currently have?\"",{},{"id":780,"data":781,"type":224,"tunes":783},"RKQXMbXA29",{"text":782},"Esa información es específica del usuario y dinámica. Se activa el Disparador de Recuperación. El sistema debe inspeccionar los datos reales de la cuenta o de autorización.",{},{"id":785,"data":786,"type":295,"tunes":788},"b5tNqxBKir",{"code":787},"\"Why was my production deployment rejected yesterday?\"",{},{"id":790,"data":791,"type":224,"tunes":793},"xaP7a7lV3i",{"text":792},"El modelo puede entender los sistemas de despliegue y explicar razones comunes. Pero la pregunta se refiere a un evento particular. Se requieren registros, salida de CI\u002FCD o registros de incidentes.",{},{"id":795,"data":796,"type":224,"tunes":798},"SlBdofaCVq",{"text":797},"La misma lógica se aplica a la búsqueda web.",{},{"id":800,"data":801,"type":295,"tunes":803},"VgFaQjUMnU",{"code":802},"\"What is RAG?\"",{},{"id":805,"data":806,"type":224,"tunes":808},"6BG7aSJQzt",{"text":807},"Una explicación general puede no requerir recuperación.",{},{"id":810,"data":811,"type":295,"tunes":813},"TRngQB41uY",{"code":812},"\"What did the authors of Self-RAG specifically conclude about unnecessary retrieval?\"",{},{"id":815,"data":816,"type":224,"tunes":818},"7UIuDjIRyG",{"text":817},"Ahora se requiere evidencia específica de la fuente.",{},{"id":820,"data":821,"type":295,"tunes":823},"CG2PbVS1yz",{"code":822},"\"What is the latest research on adaptive retrieval?\"",{},{"id":825,"data":826,"type":224,"tunes":828},"G1gyMnE_E8",{"text":827},"Esto también introduce un requisito de actualidad. El tema subyacente no ha cambiado. El requisito de información sí.",{},{"id":830,"data":831,"type":42,"tunes":833},"NyJtHsPsSf",{"text":832,"level":218},"Conceptos erróneos comunes y modos de fallo",{},{"id":835,"data":836,"type":224,"tunes":838},"1otM6VenxR",{"text":837},"Más recuperación produce automáticamente una mejor respuesta. No es así. Los documentos irrelevantes consumen contexto y pueden distraer la generación.",{},{"id":840,"data":841,"type":224,"tunes":843},"7BpMfX7lOZ",{"text":842},"Una alta confianza del modelo significa que la recuperación es innecesaria. Un modelo puede producir una respuesta incorrecta con confianza. Por lo tanto, la confianza autoinformada no debe tratarse como el único disparador.",{},{"id":845,"data":846,"type":224,"tunes":848},"THz75XkfrR",{"text":847},"Una recuperación exitosa significa que la respuesta está verificada. La recuperación solo proporciona evidencia candidata. La evidencia aún debe ser relevante, suficientemente autorizada e interpretada correctamente.",{},{"id":850,"data":851,"type":224,"tunes":853},"gOUGv2dAaq",{"text":852},"RAG resuelve automáticamente el conocimiento desactualizado. Solo lo hace si el propio corpus de recuperación contiene información actual. Recuperar un documento desactualizado no crea una respuesta actual.",{},{"id":855,"data":856,"type":224,"tunes":858},"Mz8i-je--k",{"text":857},"Un paso de recuperación siempre es suficiente. Las preguntas complejas pueden requerir varias piezas de evidencia o recuperación iterativa.",{},{"id":860,"data":861,"type":42,"tunes":863},"imAEotM35y",{"text":862,"level":218},"Casos límite",{},{"id":865,"data":866,"type":224,"tunes":868},"8xkcG8hc9c",{"text":867},"Algunas preguntas contienen información tanto estable como inestable.",{},{"id":870,"data":871,"type":295,"tunes":873},"IYDiRezoWn",{"code":872},"\"Who founded NVIDIA, and what is its market capitalization today?\"",{},{"id":875,"data":876,"type":224,"tunes":878},"2kxOM8vxYh",{"text":877},"La primera parte puede ser respondible a partir del conocimiento estable del modelo. La segunda parte requiere información actual.",{},{"id":880,"data":881,"type":224,"tunes":883},"6PyzlxURFS",{"text":882},"Un sistema suficientemente capaz no debería tratar necesariamente toda la consulta como una única decisión de recuperación. Puede activar la recuperación solo donde sea necesario.",{},{"id":885,"data":886,"type":224,"tunes":888},"lsbZ8aQAD6",{"text":887},"Otro caso límite es el desacuerdo entre fuentes. Supongamos que la recuperación devuelve tres documentos que hacen afirmaciones incompatibles.",{},{"id":890,"data":891,"type":224,"tunes":893},"-Y67JvJusX",{"text":892},"El Disparador de Recuperación ya ha tenido éxito: el sistema reconoció que se requería evidencia externa. Pero la tarea no ha terminado.",{},{"id":895,"data":896,"type":224,"tunes":898},"32VdDErqUM",{"text":897},"El sistema ha alcanzado ahora un problema de evaluación de evidencia. Aquí es donde el Límite de Validez de la Respuesta se vuelve importante.",{},{"id":900,"data":901,"type":224,"tunes":903},"edCyD-PqlU",{"text":902},"El sistema puede haber recuperado información y aún no poseer evidencia suficiente para llegar a una conclusión sólida.",{},{"id":905,"data":906,"type":295,"tunes":908},"rmjW0MBcFo",{"code":907},"Retrieval Trigger\n≠\npermission to answer",{},{"id":910,"data":911,"type":224,"tunes":913},"gZBq0voX0-",{"text":912},"El disparador obtiene evidencia. El límite de validez determina si esa evidencia es suficiente.",{},{"id":915,"data":916,"type":42,"tunes":918},"DmO9cFY93l",{"text":917,"level":218},"Limitaciones",{},{"id":920,"data":921,"type":224,"tunes":923},"gV4YT_2O1X",{"text":922},"El Disparador de Recuperación es un marco conceptual, no un algoritmo universal.",{},{"id":925,"data":926,"type":224,"tunes":928},"7c6OA2X4-H",{"text":927},"Diferentes sistemas requerirán diferentes reglas de activación. Un bot de atención al cliente, un asistente de investigación científica, un motor de búsqueda y un agente de software autónomo no tienen requisitos de evidencia idénticos.",{},{"id":930,"data":931,"type":224,"tunes":933},"Xn8K4ArjdA",{"text":932},"Los umbrales de activación también pueden crear sus propios modos de fallo. Un umbral demasiado bajo causa una recuperación excesiva. Un umbral demasiado alto causa respuestas sin respaldo.",{},{"id":935,"data":936,"type":224,"tunes":938},"y0gYRFZx6m",{"text":937},"La infraestructura de recuperación en sí también importa. Un disparador perfecto conectado a una colección de fuentes pobre sigue produciendo evidencia pobre.",{},{"id":940,"data":941,"type":224,"tunes":943},"Kcvx1v4Z1x",{"text":942},"De manera similar, una base de conocimiento excelente proporciona poco valor si el disparador nunca se activa cuando se necesita.",{},{"id":945,"data":946,"type":224,"tunes":948},"wcRpKvBhkb",{"text":947},"El Disparador de Recuperación, por lo tanto, resuelve solo una parte de una arquitectura más amplia.",{},{"id":950,"data":951,"type":42,"tunes":953},"YN1_g7vs7V",{"text":952,"level":218},"¿Qué cambiaría esta respuesta?",{},{"id":955,"data":956,"type":224,"tunes":958},"4PYX_PYK_Z",{"text":957},"Los modelos futuros pueden contener mejores mecanismos para identificar sus propias limitaciones de conocimiento. Los recuperadores pueden volverse más baratos y rápidos. Los sistemas de contexto largo pueden llevar mucho más material fuente de forma continua.",{},{"id":960,"data":961,"type":224,"tunes":963},"vyqJ8Kp8Ye",{"text":962},"Los modelos también pueden combinar cada vez más búsqueda, bases de datos, herramientas y conocimiento estructurado sin exponer una etapa RAG distinta al desarrollador de la aplicación.",{},{"id":965,"data":966,"type":224,"tunes":968},"_KN0MPs6as",{"text":967},"Estos cambios podrían alterar cómo se implementa el disparador. No necesariamente eliminan la decisión subyacente.",{},{"id":970,"data":971,"type":224,"tunes":973},"Zrlr4a0utJ",{"text":972},"Siempre que exista una diferencia entre la información ya disponible para el modelo y la información que debe obtenerse externamente, un sistema aún necesita algún mecanismo para determinar cuándo cruzar esa frontera.",{},{"id":975,"data":976,"type":224,"tunes":978},"4hl7r5cF1M",{"text":977},"La implementación puede desaparecer de la vista. La cuestión arquitectónica permanece.",{},{"id":980,"data":981,"type":42,"tunes":983},"o_g5g_6eSj",{"text":982,"level":218},"Conclusión",{},{"id":985,"data":986,"type":224,"tunes":988},"L6MWm8xdAg",{"text":987},"RAG comienza demasiado tarde para explicar todo el problema.",{},{"id":990,"data":991,"type":224,"tunes":993},"uymgoYlFM5",{"text":992},"Antes de que pueda ocurrir la recuperación, un sistema de IA debe determinar si la recuperación es necesaria. Esa decisión es el Disparador de Recuperación.",{},{"id":995,"data":996,"type":295,"tunes":998},"_KIblg0ae_",{"code":997},"Stable known fact\n→ answer from model knowledge\n\nCurrent fact\n→ retrieve\n\nSource-specific or evidence-dependent claim\n→ retrieve and verify",{},{"id":1000,"data":1001,"type":224,"tunes":1003},"unCgfbeYI5",{"text":1002},"Pero la implicación más amplia es más importante. La IA confiable no solo necesita acceso al conocimiento. Necesita un método para determinar cuándo su conocimiento actual es insuficiente.",{},{"id":1005,"data":1006,"type":295,"tunes":1008},"ZAosU4trn9",{"code":1007},"Model Knowledge\n        ↓\nRetrieval Trigger\n        ↓\nRuntime Knowledge \u002F RAG\n        ↓\nEvidence\n        ↓\nReasoning\n        ↓\nAnswer Validity Boundary\n        ↓\nAnswer",{},{"id":1010,"data":1011,"type":224,"tunes":1013},"f0ZIysaJy1",{"text":1012},"El Disparador de Recuperación determina cuándo el sistema debe buscar evidencia. El Límite de Validez de la Respuesta determina si esa evidencia es suficiente.",{},{"id":1015,"data":1016,"type":224,"tunes":1018},"iCg9ojv75m",{"text":1017},"Juntos describen algo más útil que RAG por sí solo: un proceso de decisión para pasar de lo que una IA parece saber hacia lo que realmente puede respaldar.",{},{"id":1020,"data":1021,"type":42,"tunes":1023},"cDBiNnZJv-",{"text":1022,"level":218},"Fuentes Primarias",{},{"id":1025,"data":1026,"type":224,"tunes":1028},"8gumvODB16",{"text":1027},"Patrick Lewis et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\u003C\u002Fa> (2020). Trabajo fundacional de RAG que describe la combinación de memoria paramétrica del modelo con memoria no paramétrica externa.",{},{"id":1030,"data":1031,"type":224,"tunes":1033},"Chz6I7zmlv",{"text":1032},"Zhengbao Jiang et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">Active Retrieval Augmented Generation\u003C\u002Fa> (2023). Introduce FLARE y la recuperación activa durante la generación, incluida la recuperación basada en contenido predicho con baja confianza.",{},{"id":1035,"data":1036,"type":224,"tunes":1038},"MRdjivpsoW",{"text":1037},"Akari Asai et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection\u003C\u002Fa> (2023). Explora la recuperación adaptativa bajo demanda y la autorreflexión en lugar de la recuperación fija incondicional.",{},{"id":1040,"data":1041,"type":224,"tunes":1043},"lck29euXJP",{"text":1042},"Soyeong Jeong et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity\u003C\u002Fa> (2024). Selecciona dinámicamente entre sin recuperación, recuperación de un solo paso y estrategias de recuperación más complejas según la pregunta entrante.",{},"2.31","Un modelo de IA no necesita recuperación para cada pregunta. El problema importante es saber cuándo su conocimiento interno ya no es suficiente. El Disparador de Recuperación es un límite de decisión práctico que determina cuándo un sistema de IA debe dejar de depender únicamente del conocimiento del modelo y obtener evidencia externa antes de responder.","\u002Fuploads\u002F2026\u002F09\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger-1790574991244-f4rpyg.webp","when-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger-1790574991244-f4rpyg","PUBLISHED","2026-09-28T01:49:00.000Z","2026-09-28T05:49:59.593Z","2026-09-28T06:02:54.212Z",{"en":1053,"de":1054,"sr":1055,"es":1056,"fr":1057,"it":1058,"ru":1059,"zh":1060},"\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fde\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fsr\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fes\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Ffr\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fit\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fru\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fzh\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger",[1062,1065,1069,1073,1077,1081],{"id":101,"name":1063,"slug":1064},"Overview","overview-digital-platform",{"id":1066,"name":1067,"slug":1068},57,"Límites de datos","data-boundaries",{"id":1070,"name":1071,"slug":1072},51,"Antipatrones","anti-patterns",{"id":1074,"name":1075,"slug":1076},58,"Evaluación y compuertas de calidad","evaluation",{"id":1078,"name":1079,"slug":1080},56,"Portafolio de casos de uso","use-case-portfolio",{"id":1082,"name":1083,"slug":1084},60,"Controles de coste y latencia","cost-and-latency",{"id":1086,"login":1087,"email":1088,"displayName":1089},"20","rooth8233","aleksandar@stajic.de","Aleksandar Stajić",[1091,1738],{"lang":1092,"title":1093,"content":1094,"contentJson":1095,"excerpt":1737},"en","When Should an AI Stop Trusting Its Own Knowledge? — The Retrieval Trigger","{\"time\":1790574879391,\"blocks\":[{\"id\":\"Wt7UfNeFlS\",\"data\":{\"text\":\"Question\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"T-ZCQblBzm\",\"data\":{\"text\":\"When should an AI stop relying on what it already knows and retrieve external information before answering?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"vBcd4061WS\",\"data\":{\"text\":\"This question appears simple, but it sits at the center of one of the most important design decisions in modern AI systems.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"r9NZ-Fzw0e\",\"data\":{\"text\":\"Large language models contain substantial knowledge in their parameters. Retrieval-Augmented Generation adds external information at runtime. But neither extreme is ideal.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"CpzlgJjAVL\",\"data\":{\"text\":\"Always trusting the model can produce outdated or unsupported answers. Always retrieving information adds latency, cost, irrelevant context and new opportunities for retrieval errors.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"yeclJhYJ1a\",\"data\":{\"text\":\"The real problem therefore comes before RAG: When should retrieval happen at all?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"FgLSWvpZMg\",\"data\":{\"text\":\"This article uses the term Retrieval Trigger for that decision. Retrieval Trigger is not presented here as a standardized term from the research literature. It is a practical systems concept that brings together ideas already visible in research on active, adaptive and self-reflective retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Muzvv-2uzU\",\"data\":{\"text\":\"A Retrieval Trigger is a condition indicating that an AI system should stop relying solely on internal model knowledge and obtain external evidence before producing or finalizing an answer.\",\"caption\":\"Working definition\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"1BGt1waZ01\",\"data\":{\"title\":\"Contents\",\"maxLevel\":3,\"minLevel\":2},\"type\":\"tableOfContents\",\"tunes\":{}},{\"id\":\"BFKJ2htjYN\",\"data\":{\"text\":\"What This Really Means\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"yfBYqVwObv\",\"data\":{\"text\":\"An LLM has two fundamentally different ways of obtaining information.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Y4JYebztDi\",\"data\":{\"text\":\"The first is model knowledge. This is information represented in the model's learned parameters. No database query, web search or document lookup is required at runtime.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"x2L37FSTBK\",\"data\":{\"text\":\"The second is runtime knowledge. This is information provided while the model is operating: search results, database records, documents, APIs, user files, tool outputs or other retrieved evidence.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"2szDUb7_-4\",\"data\":{\"text\":\"RAG connects these two worlds. But RAG itself does not answer the question of when that connection should be activated. That is the purpose of the Retrieval Trigger.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"5_yjTthHV4\",\"data\":{\"code\":\"Question\\n   ↓\\nModel Knowledge\\n   ↓\\nIs internal knowledge sufficient?\\n   ↓\\nRetrieval Trigger\\n   ↓\\nExternal Retrieval, if required\\n   ↓\\nEvidence\\n   ↓\\nReasoning\\n   ↓\\nAnswer Validity Boundary\\n   ↓\\nAnswer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"rH2K36ambR\",\"data\":{\"text\":\"The Retrieval Trigger therefore sits before retrieval. The Answer Validity Boundary sits later.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"L0WlGs_dTF\",\"data\":{\"text\":\"The first asks: Do I need external evidence?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"JyE4O9aDCW\",\"data\":{\"text\":\"The second asks: Do I now have enough evidence to support this answer?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"L9JP5xByy4\",\"data\":{\"text\":\"These are related decisions, but they are not the same decision.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"4hPbiDSHek\",\"data\":{\"text\":\"Simplest Example\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"cER32Me6gA\",\"data\":{\"text\":\"Consider three questions.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"izi7nU9FE9\",\"data\":{\"content\":[[\"Question\",\"Internal knowledge\",\"Retrieval Trigger\"],[\"What is the capital of France?\",\"Usually sufficient\",\"No strong trigger\"],[\"What is the current NVIDIA stock price?\",\"Potentially outdated\",\"Trigger retrieval\"],[\"Does this new scientific paper prove that X causes Y?\",\"Cannot establish the claim without examining the evidence\",\"Strong retrieval trigger\"]],\"stretched\":false,\"withHeadings\":true},\"type\":\"table\",\"tunes\":{}},{\"id\":\"cb-Kx0fKs4\",\"data\":{\"text\":\"The first question is based on a highly stable fact.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"MZJzwvZUH7\",\"data\":{\"code\":\"User\\n↓\\n\\\"What is the capital of France?\\\"\\n\\nModel knowledge\\n↓\\nParis\\n\\nFresh external evidence required?\\n↓\\nNo\\n\\nAnswer\\n↓\\nParis\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"fRP7-aWTJB\",\"data\":{\"text\":\"Retrieving documents before answering would usually add little value.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"O2TaSvLoxO\",\"data\":{\"text\":\"Now consider a question whose answer changes continuously.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"cNv0Dp7Mk3\",\"data\":{\"code\":\"User\\n↓\\n\\\"What is the current NVIDIA stock price?\\\"\\n\\nModel knowledge\\n↓\\nPotentially outdated\\n\\nCurrent information required?\\n↓\\nYes\\n\\nRETRIEVAL TRIGGER\\n↓\\nMarket data \u002F search \u002F API\\n↓\\nAnswer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"Y3NDw8awnA\",\"data\":{\"text\":\"The model may know a great deal about NVIDIA. That does not mean it knows the price now.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"FwjiaA6mdJ\",\"data\":{\"text\":\"The third example is even more important.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"G48ZGtX4XK\",\"data\":{\"code\":\"User\\n↓\\n\\\"Does this new scientific paper prove that X causes Y?\\\"\\n\\nModel knowledge\\n↓\\nCan reason about causality,\\nstatistics and scientific methodology.\\n\\nBut:\\nthe actual evidence is not available internally.\\n\\nRETRIEVAL TRIGGER\\n↓\\nRetrieve the paper\\n↓\\nInspect methodology\\n↓\\nInspect results\\n↓\\nCompare claim with evidence\\n↓\\nAnswer Validity Boundary\\n↓\\nAnswer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"nGu-KcQC6l\",\"data\":{\"text\":\"The model's reasoning capability may be perfectly useful. The missing component is evidence.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"_bUxnOYvHG\",\"data\":{\"text\":\"That distinction is fundamental.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"etbE_esRx4\",\"data\":{\"text\":\"Where the Example Stops Working\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"0iSdy2Msw7\",\"data\":{\"text\":\"The examples above make the decision appear binary: retrieve or do not retrieve.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"8Go7nm2niJ\",\"data\":{\"text\":\"Real systems are more complicated. A question may contain several claims, some stable and some current. Retrieved documents may disagree. A retriever may return irrelevant information. The relevant information may exist but fail to rank highly enough. A document may be authoritative but outdated.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"8dVjRU5cXg\",\"data\":{\"text\":\"Retrieval itself can also introduce incorrect context into an otherwise reasonable answer.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"pLqSH5-OJR\",\"data\":{\"text\":\"This is why retrieval should not be treated as an automatic synonym for truth.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"ww4Od2cmTr\",\"data\":{\"text\":\"Research on adaptive retrieval has increasingly moved away from the assumption that every query should receive the same retrieval strategy.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"1yE2LUP7cF\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\\\" target=\\\"_blank\\\">Self-RAG\u003C\u002Fa>, for example, explicitly explores retrieval on demand rather than indiscriminately retrieving a fixed number of passages for every input. The authors discuss how unnecessary or irrelevant retrieval can reduce answer quality.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"915QBDW89m\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\\\" target=\\\"_blank\\\">Adaptive-RAG\u003C\u002Fa> similarly selects between no retrieval, single-step retrieval and more complex retrieval strategies according to question complexity.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"1FBgxY0QQp\",\"data\":{\"text\":\"So the important question is not: Does this system have RAG?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"4xdj86u8Qz\",\"data\":{\"text\":\"It is: Can this system recognize when retrieval is necessary and what kind of retrieval is appropriate?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"bGPa0AsJI6\",\"data\":{\"text\":\"Direct Answer\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"fBKcyJ0IcX\",\"data\":{\"text\":\"An AI should trigger retrieval when answering requires information that its internal model knowledge cannot safely provide with the required freshness, specificity, provenance or evidential support.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"JbIPIJjkXK\",\"data\":{\"text\":\"In practical systems, a Retrieval Trigger can emerge from several conditions:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"_JbTSHlrtH\",\"data\":{\"code\":\"Need for current information\\n        OR\\nNeed for exact source-specific information\\n        OR\\nNeed for evidence or provenance\\n        OR\\nNeed for private\u002Fuser-specific information\\n        OR\\nInsufficient knowledge coverage\\n        OR\\nConflicting evidence\\n        OR\\nHigh consequence of factual error\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"Aaem6fQ_tF\",\"data\":{\"text\":\"If none of these conditions is materially present, retrieval may be unnecessary. If one or more are present, external evidence becomes part of the answer-generation process.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"x2DDg7Ue1-\",\"data\":{\"text\":\"Why This Is So\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"1GTaWG9ViB\",\"data\":{\"text\":\"A language model's internal knowledge is often described as parametric knowledge. It was learned during training and encoded into the model's parameters.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"klwNY3lr1d\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\\\" target=\\\"_blank\\\">Lewis et al.'s original RAG work\u003C\u002Fa> framed retrieval as a combination of this parametric memory with external, non-parametric memory. The external memory can be searched and updated without retraining the entire language model.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Fyw2AVDbxR\",\"data\":{\"text\":\"This distinction creates an unavoidable systems problem.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"4FvbthV3in\",\"data\":{\"text\":\"The model can know things. But the model cannot assume that everything it knows is current, complete, specific enough and supported by the required evidence.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"asxdihTbcB\",\"data\":{\"text\":\"A model can therefore produce a linguistically convincing answer while still operating beyond the point where its internal knowledge is sufficient.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"jGgq116uAa\",\"data\":{\"text\":\"That point is where a Retrieval Trigger becomes useful.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"T6q_BUeDg3\",\"data\":{\"text\":\"Context\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"9H_bNlyoYs\",\"data\":{\"text\":\"Traditional RAG often looks like this:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"YSZR1AInSj\",\"data\":{\"code\":\"Question\\n↓\\nRetrieve documents\\n↓\\nAdd documents to context\\n↓\\nGenerate answer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"HnzY2Q9xTs\",\"data\":{\"text\":\"This architecture assumes retrieval before generation. That works well for many knowledge-intensive applications, but it can also perform unnecessary retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Aho03YTGAU\",\"data\":{\"text\":\"More advanced approaches introduce an adaptive step:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"uK0l0tYLg6\",\"data\":{\"code\":\"Question\\n↓\\nEvaluate information requirement\\n↓\\n        ┌───────────────┐\\n        │               │\\n   no retrieval      retrieval\\n        │               │\\n        ↓               ↓\\n model knowledge    external evidence\\n        │               │\\n        └───────┬───────┘\\n                ↓\\n              answer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"Upb-15aN8T\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\\\" target=\\\"_blank\\\">FLARE\u003C\u002Fa> goes further by considering retrieval during generation itself. It uses upcoming generation and low-confidence tokens as signals for retrieving additional information.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"I5hs5j9IKc\",\"data\":{\"text\":\"Self-RAG similarly introduces mechanisms allowing retrieval, generation and critique to interact instead of treating retrieval as an unconditional preprocessing step.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"mw2jbuWA-g\",\"data\":{\"text\":\"Adaptive-RAG approaches the same broader problem from query complexity: different questions may require different retrieval strategies.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"DUba0EfbWg\",\"data\":{\"text\":\"These approaches differ technically. But they expose the same architectural insight: Retrieval should be a decision, not merely a permanent switch.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"wzX0jC8H8b\",\"data\":{\"text\":\"Assumptions\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"4puAk8h-NF\",\"data\":{\"text\":\"The Retrieval Trigger framework assumes that a system has access to at least one external information source when retrieval is required.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Qsm42lc7aC\",\"data\":{\"text\":\"That source could be web search, a document store, vector database, SQL database, knowledge graph, API, enterprise system, user-uploaded document or tool output.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Rznt7yvqT2\",\"data\":{\"text\":\"It also assumes that retrieval has a cost. That cost does not have to be financial.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"wQoEfZuFPe\",\"data\":{\"text\":\"Retrieval introduces latency, token consumption, context usage, infrastructure complexity and the possibility of retrieving misleading information.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"WM1F9QkT2G\",\"data\":{\"text\":\"The optimal system therefore does not maximize retrieval. It maximizes appropriate retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Z4gw9SX7jo\",\"data\":{\"text\":\"Variables\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"Z_sKNO6vmp\",\"data\":{\"text\":\"A practical Retrieval Trigger can consider five primary variables.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Eti88tz1T6\",\"data\":{\"text\":\"Freshness\",\"level\":3},\"type\":\"header\",\"tunes\":{}},{\"id\":\"3zKe198lls\",\"data\":{\"text\":\"How likely is the required information to have changed? The capital of France has very low volatility. A stock price has extremely high volatility.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"ryQRR7TzC7\",\"data\":{\"text\":\"Specificity\",\"level\":3},\"type\":\"header\",\"tunes\":{}},{\"id\":\"bkXBBuCBb_\",\"data\":{\"text\":\"Does the question require information from a particular source, document, organization, account or dataset? If the user asks what a specific contract says, general model knowledge is irrelevant. The contract must be retrieved.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"LlT6c-tPU2\",\"data\":{\"text\":\"Evidence Requirement\",\"level\":3},\"type\":\"header\",\"tunes\":{}},{\"id\":\"1G-aWjGT1c\",\"data\":{\"text\":\"Does the answer need provenance? A model may know that a claim is generally accepted but still need a source when the task requires verification.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"lnoOCm4KDw\",\"data\":{\"text\":\"Knowledge Coverage\",\"level\":3},\"type\":\"header\",\"tunes\":{}},{\"id\":\"nKGrZO0Zw0\",\"data\":{\"text\":\"Is the subject likely to be represented adequately in internal model knowledge? Rare, proprietary, highly local or newly published information creates stronger retrieval pressure.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"SYp_4G0qXz\",\"data\":{\"text\":\"Consequence of Error\",\"level\":3},\"type\":\"header\",\"tunes\":{}},{\"id\":\"YJeo8nKsl9\",\"data\":{\"text\":\"Not every incorrect answer has the same impact. Where factual accuracy materially affects a decision, the acceptable evidence threshold may be higher.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"NTh27HJjo1\",\"data\":{\"text\":\"These variables do not have to be implemented as literal numeric scores. They describe the decision surface.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"A25id0cm1s\",\"data\":{\"text\":\"Diagnostic \u002F Decision Method\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"az70f7cIIF\",\"data\":{\"text\":\"A very simple Retrieval Trigger can be implemented without machine learning.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"yUFgVx9VRM\",\"data\":{\"code\":\"def should_retrieve(\\n    time_sensitive=False,\\n    source_specific=False,\\n    evidence_required=False,\\n    private_context=False,\\n    knowledge_uncertain=False,\\n    conflicting_information=False\\n):\\n    return any([\\n        time_sensitive,\\n        source_specific,\\n        evidence_required,\\n        private_context,\\n        knowledge_uncertain,\\n        conflicting_information,\\n    ])\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"tBn6sOGnKB\",\"data\":{\"text\":\"For a stable factual question:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"BW2rsTbqqL\",\"data\":{\"code\":\"should_retrieve()\\n# False\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"iriE0iq97f\",\"data\":{\"text\":\"For a current stock price:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"d10aolm-TW\",\"data\":{\"code\":\"should_retrieve(\\n    time_sensitive=True\\n)\\n# True\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"nwL_vpUi-Y\",\"data\":{\"text\":\"For a scientific claim:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"-DXgs4BBKH\",\"data\":{\"code\":\"should_retrieve(\\n    source_specific=True,\\n    evidence_required=True\\n)\\n# True\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"Wj1sAbZ7l8\",\"data\":{\"text\":\"Production systems can make this decision far more sophisticated. A classifier could predict retrieval requirements. A model could emit special control tokens. A router could classify query complexity. Retrieval could also be triggered repeatedly during generation.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"loLbe4TkAK\",\"data\":{\"text\":\"The implementation can change. The architectural question remains the same:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"T2PJWaSYp9\",\"data\":{\"text\":\"Is the evidence currently available to the model sufficient for the answer it is about to produce?\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"9Alw1zzG4E\",\"data\":{\"text\":\"Evidence\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"OgqdUwG1J-\",\"data\":{\"text\":\"The concept proposed here is consistent with several lines of retrieval research.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"QxIkEDnlBu\",\"data\":{\"text\":\"The original \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\\\" target=\\\"_blank\\\">RAG architecture\u003C\u002Fa> demonstrated the usefulness of combining parametric model knowledge with external non-parametric knowledge, particularly for knowledge-intensive tasks.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"nqdi_kDLE3\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\\\" target=\\\"_blank\\\">FLARE\u003C\u002Fa> explicitly explores active retrieval during generation, including retrieval prompted by low-confidence upcoming content.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"UThAFgyEe3\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\\\" target=\\\"_blank\\\">Self-RAG\u003C\u002Fa> demonstrates an architecture in which retrieval can occur on demand and is followed by reflection on retrieved passages and generated content.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"CT8n5F5KLR\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\\\" target=\\\"_blank\\\">Adaptive-RAG\u003C\u002Fa> dynamically chooses among different strategies according to question complexity, including situations where no retrieval is required.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"RDjo1UGf2s\",\"data\":{\"text\":\"The term Retrieval Trigger is used here as a system-level abstraction over this broader family of decisions.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"nmWZ-exi8b\",\"data\":{\"text\":\"It does not claim that these papers use the same terminology. Instead, it identifies the shared architectural problem: What causes an AI system to transition from internal knowledge to external evidence?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"8a-H_OlfG1\",\"data\":{\"text\":\"Real Examples\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"l0KONt5Buo\",\"data\":{\"text\":\"Consider a support assistant connected to a company's documentation.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"MOy11BOFq5\",\"data\":{\"code\":\"\\\"How do I reset my password?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"tsZcuMS1sT\",\"data\":{\"text\":\"If the procedure is stable and reliably represented in the assistant's current instructions, direct answering may be appropriate.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"l53NPB6WNV\",\"data\":{\"code\":\"\\\"What permissions does my account currently have?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"RKQXMbXA29\",\"data\":{\"text\":\"That information is user-specific and dynamic. The Retrieval Trigger fires. The system must inspect the actual account or authorization data.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"b5tNqxBKir\",\"data\":{\"code\":\"\\\"Why was my production deployment rejected yesterday?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"xaP7a7lV3i\",\"data\":{\"text\":\"The model can understand deployment systems and explain common reasons. But the question is asking about a particular event. Logs, CI\u002FCD output or incident records are required.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"SlBdofaCVq\",\"data\":{\"text\":\"The same logic works for web search.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"VgFaQjUMnU\",\"data\":{\"code\":\"\\\"What is RAG?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"6BG7aSJQzt\",\"data\":{\"text\":\"A general explanation may not require retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"TRngQB41uY\",\"data\":{\"code\":\"\\\"What did the authors of Self-RAG specifically conclude about unnecessary retrieval?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"7UIuDjIRyG\",\"data\":{\"text\":\"Now source-specific evidence is required.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"CG2PbVS1yz\",\"data\":{\"code\":\"\\\"What is the latest research on adaptive retrieval?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"G1gyMnE_E8\",\"data\":{\"text\":\"This introduces a freshness requirement as well. The underlying subject has not changed. The information requirement has.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"NyJtHsPsSf\",\"data\":{\"text\":\"Common Misconceptions and Failure Modes\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"1otM6VenxR\",\"data\":{\"text\":\"More retrieval automatically produces a better answer. It does not. Irrelevant documents consume context and can distract generation.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"7BpMfX7lOZ\",\"data\":{\"text\":\"High model confidence means retrieval is unnecessary. A model can produce an incorrect answer confidently. Self-reported confidence should therefore not be treated as the only trigger.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"THz75XkfrR\",\"data\":{\"text\":\"Successful retrieval means the answer is verified. Retrieval only provides candidate evidence. The evidence must still be relevant, sufficiently authoritative and correctly interpreted.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"gOUGv2dAaq\",\"data\":{\"text\":\"RAG automatically solves outdated knowledge. It only does so if the retrieval corpus itself contains current information. Retrieving an outdated document does not create a current answer.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Mz8i-je--k\",\"data\":{\"text\":\"One retrieval step is always enough. Complex questions may require several pieces of evidence or iterative retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"imAEotM35y\",\"data\":{\"text\":\"Edge Cases\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"8xkcG8hc9c\",\"data\":{\"text\":\"Some questions contain both stable and unstable information.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"IYDiRezoWn\",\"data\":{\"code\":\"\\\"Who founded NVIDIA, and what is its market capitalization today?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"2kxOM8vxYh\",\"data\":{\"text\":\"The first part may be answerable from stable model knowledge. The second part requires current information.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"6PyzlxURFS\",\"data\":{\"text\":\"A sufficiently capable system should not necessarily treat the entire query as one retrieval decision. It can trigger retrieval only where required.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"lsbZ8aQAD6\",\"data\":{\"text\":\"Another edge case is disagreement between sources. Suppose retrieval returns three documents making incompatible claims.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"-Y67JvJusX\",\"data\":{\"text\":\"The Retrieval Trigger has already succeeded: the system recognized that external evidence was required. But the task is not finished.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"32VdDErqUM\",\"data\":{\"text\":\"The system has now reached an evidence evaluation problem. This is where the Answer Validity Boundary becomes important.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"edCyD-PqlU\",\"data\":{\"text\":\"The system may have retrieved information and still not possess enough evidence to make a strong conclusion.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"rmjW0MBcFo\",\"data\":{\"code\":\"Retrieval Trigger\\n≠\\npermission to answer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"gZBq0voX0-\",\"data\":{\"text\":\"The trigger obtains evidence. The validity boundary determines whether that evidence is sufficient.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"DmO9cFY93l\",\"data\":{\"text\":\"Limitations\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"gV4YT_2O1X\",\"data\":{\"text\":\"The Retrieval Trigger is a conceptual framework, not a universal algorithm.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"7c6OA2X4-H\",\"data\":{\"text\":\"Different systems will require different trigger rules. A customer-support bot, scientific research assistant, search engine and autonomous software agent do not have identical evidence requirements.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Xn8K4ArjdA\",\"data\":{\"text\":\"Trigger thresholds can also create their own failure modes. A threshold that is too low causes excessive retrieval. A threshold that is too high causes unsupported answering.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"y0gYRFZx6m\",\"data\":{\"text\":\"The retrieval infrastructure itself also matters. A perfect trigger connected to a poor source collection still produces poor evidence.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Kcvx1v4Z1x\",\"data\":{\"text\":\"Similarly, an excellent knowledge base provides little value if the trigger never activates when it is needed.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"wcRpKvBhkb\",\"data\":{\"text\":\"The Retrieval Trigger therefore solves only one part of a larger architecture.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"YN1_g7vs7V\",\"data\":{\"text\":\"What Would Change This Answer?\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"4PYX_PYK_Z\",\"data\":{\"text\":\"Future models may contain better mechanisms for identifying their own knowledge limitations. Retrievers may become cheaper and faster. Long-context systems may carry far more source material continuously.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"vyqJ8Kp8Ye\",\"data\":{\"text\":\"Models may also increasingly combine search, databases, tools and structured knowledge without exposing a distinct RAG stage to the application developer.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"_KN0MPs6as\",\"data\":{\"text\":\"These changes could alter how the trigger is implemented. They do not necessarily remove the underlying decision.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Zrlr4a0utJ\",\"data\":{\"text\":\"As long as there is a difference between information already available to the model and information that must be obtained externally, a system still needs some mechanism for determining when to cross that boundary.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"4hl7r5cF1M\",\"data\":{\"text\":\"The implementation may disappear from view. The architectural question remains.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"o_g5g_6eSj\",\"data\":{\"text\":\"Conclusion\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"L6MWm8xdAg\",\"data\":{\"text\":\"RAG begins too late to explain the whole problem.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"uymgoYlFM5\",\"data\":{\"text\":\"Before retrieval can happen, an AI system must determine whether retrieval is necessary. That decision is the Retrieval Trigger.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"_KIblg0ae_\",\"data\":{\"code\":\"Stable known fact\\n→ answer from model knowledge\\n\\nCurrent fact\\n→ retrieve\\n\\nSource-specific or evidence-dependent claim\\n→ retrieve and verify\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"unCgfbeYI5\",\"data\":{\"text\":\"But the broader implication is more important. Reliable AI does not merely need access to knowledge. It needs a method for determining when its current knowledge is insufficient.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"ZAosU4trn9\",\"data\":{\"code\":\"Model Knowledge\\n        ↓\\nRetrieval Trigger\\n        ↓\\nRuntime Knowledge \u002F RAG\\n        ↓\\nEvidence\\n        ↓\\nReasoning\\n        ↓\\nAnswer Validity Boundary\\n        ↓\\nAnswer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"f0ZIysaJy1\",\"data\":{\"text\":\"The Retrieval Trigger determines when the system should seek evidence. The Answer Validity Boundary determines whether that evidence is sufficient.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"iCg9ojv75m\",\"data\":{\"text\":\"Together they describe something more useful than RAG alone: a decision process for moving from what an AI appears to know toward what it can actually support.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"cDBiNnZJv-\",\"data\":{\"text\":\"Primary Sources\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"8gumvODB16\",\"data\":{\"text\":\"Patrick Lewis et al., \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\\\" target=\\\"_blank\\\">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\u003C\u002Fa> (2020). Foundational RAG work describing the combination of parametric model memory with external non-parametric memory.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Chz6I7zmlv\",\"data\":{\"text\":\"Zhengbao Jiang et al., \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\\\" target=\\\"_blank\\\">Active Retrieval Augmented Generation\u003C\u002Fa> (2023). Introduces FLARE and active retrieval during generation, including retrieval based on low-confidence predicted content.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"MRdjivpsoW\",\"data\":{\"text\":\"Akari Asai et al., \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\\\" target=\\\"_blank\\\">Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection\u003C\u002Fa> (2023). Explores adaptive retrieval on demand and self-reflection instead of unconditional fixed retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"lck29euXJP\",\"data\":{\"text\":\"Soyeong Jeong et al., \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\\\" target=\\\"_blank\\\">Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity\u003C\u002Fa> (2024). Dynamically selects among no retrieval, single-step retrieval and more complex retrieval strategies according to the incoming question.\"},\"type\":\"paragraph\",\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1096,"blocks":1097,"version":1736},1790574879391,[1098,1102,1106,1110,1114,1118,1122,1126,1131,1135,1139,1143,1147,1151,1155,1158,1162,1166,1170,1174,1178,1182,1201,1205,1208,1212,1216,1219,1223,1227,1230,1234,1238,1242,1246,1250,1254,1258,1262,1266,1270,1274,1278,1282,1286,1290,1293,1297,1301,1305,1309,1313,1317,1321,1325,1329,1333,1336,1340,1344,1347,1351,1355,1359,1363,1367,1371,1375,1379,1383,1387,1390,1394,1398,1402,1406,1410,1414,1418,1422,1426,1430,1434,1438,1442,1446,1449,1453,1456,1460,1463,1467,1470,1474,1478,1482,1486,1490,1494,1498,1502,1506,1510,1514,1518,1522,1525,1529,1532,1536,1539,1543,1547,1550,1554,1557,1561,1564,1568,1572,1576,1580,1584,1588,1592,1596,1600,1603,1607,1611,1615,1619,1623,1627,1630,1634,1638,1642,1646,1650,1654,1658,1662,1666,1670,1674,1678,1682,1686,1690,1694,1698,1701,1705,1708,1712,1716,1720,1724,1728,1732],{"id":215,"data":1099,"type":42,"tunes":1101},{"text":1100,"level":218},"Question",{},{"id":221,"data":1103,"type":224,"tunes":1105},{"text":1104},"When should an AI stop relying on what it already knows and retrieve external information before answering?",{},{"id":227,"data":1107,"type":224,"tunes":1109},{"text":1108},"This question appears simple, but it sits at the center of one of the most important design decisions in modern AI systems.",{},{"id":232,"data":1111,"type":224,"tunes":1113},{"text":1112},"Large language models contain substantial knowledge in their parameters. Retrieval-Augmented Generation adds external information at runtime. But neither extreme is ideal.",{},{"id":237,"data":1115,"type":224,"tunes":1117},{"text":1116},"Always trusting the model can produce outdated or unsupported answers. Always retrieving information adds latency, cost, irrelevant context and new opportunities for retrieval errors.",{},{"id":242,"data":1119,"type":224,"tunes":1121},{"text":1120},"The real problem therefore comes before RAG: When should retrieval happen at all?",{},{"id":247,"data":1123,"type":224,"tunes":1125},{"text":1124},"This article uses the term Retrieval Trigger for that decision. Retrieval Trigger is not presented here as a standardized term from the research literature. It is a practical systems concept that brings together ideas already visible in research on active, adaptive and self-reflective retrieval.",{},{"id":252,"data":1127,"type":257,"tunes":1130},{"text":1128,"caption":1129,"alignment":256},"A Retrieval Trigger is a condition indicating that an AI system should stop relying solely on internal model knowledge and obtain external evidence before producing or finalizing an answer.","Working definition",{},{"id":260,"data":1132,"type":264,"tunes":1134},{"title":1133,"maxLevel":263,"minLevel":218},"Contents",{},{"id":267,"data":1136,"type":42,"tunes":1138},{"text":1137,"level":218},"What This Really Means",{},{"id":272,"data":1140,"type":224,"tunes":1142},{"text":1141},"An LLM has two fundamentally different ways of obtaining information.",{},{"id":277,"data":1144,"type":224,"tunes":1146},{"text":1145},"The first is model knowledge. This is information represented in the model's learned parameters. No database query, web search or document lookup is required at runtime.",{},{"id":282,"data":1148,"type":224,"tunes":1150},{"text":1149},"The second is runtime knowledge. This is information provided while the model is operating: search results, database records, documents, APIs, user files, tool outputs or other retrieved evidence.",{},{"id":287,"data":1152,"type":224,"tunes":1154},{"text":1153},"RAG connects these two worlds. But RAG itself does not answer the question of when that connection should be activated. That is the purpose of the Retrieval Trigger.",{},{"id":292,"data":1156,"type":295,"tunes":1157},{"code":294},{},{"id":298,"data":1159,"type":224,"tunes":1161},{"text":1160},"The Retrieval Trigger therefore sits before retrieval. The Answer Validity Boundary sits later.",{},{"id":303,"data":1163,"type":224,"tunes":1165},{"text":1164},"The first asks: Do I need external evidence?",{},{"id":308,"data":1167,"type":224,"tunes":1169},{"text":1168},"The second asks: Do I now have enough evidence to support this answer?",{},{"id":313,"data":1171,"type":224,"tunes":1173},{"text":1172},"These are related decisions, but they are not the same decision.",{},{"id":318,"data":1175,"type":42,"tunes":1177},{"text":1176,"level":218},"Simplest Example",{},{"id":323,"data":1179,"type":224,"tunes":1181},{"text":1180},"Consider three questions.",{},{"id":328,"data":1183,"type":346,"tunes":1200},{"content":1184,"stretched":43,"withHeadings":14},[1185,1188,1192,1196],[1100,1186,1187],"Internal knowledge","Retrieval Trigger",[1189,1190,1191],"What is the capital of France?","Usually sufficient","No strong trigger",[1193,1194,1195],"What is the current NVIDIA stock price?","Potentially outdated","Trigger retrieval",[1197,1198,1199],"Does this new scientific paper prove that X causes Y?","Cannot establish the claim without examining the evidence","Strong retrieval trigger",{},{"id":349,"data":1202,"type":224,"tunes":1204},{"text":1203},"The first question is based on a highly stable fact.",{},{"id":354,"data":1206,"type":295,"tunes":1207},{"code":356},{},{"id":359,"data":1209,"type":224,"tunes":1211},{"text":1210},"Retrieving documents before answering would usually add little value.",{},{"id":364,"data":1213,"type":224,"tunes":1215},{"text":1214},"Now consider a question whose answer changes continuously.",{},{"id":369,"data":1217,"type":295,"tunes":1218},{"code":371},{},{"id":374,"data":1220,"type":224,"tunes":1222},{"text":1221},"The model may know a great deal about NVIDIA. That does not mean it knows the price now.",{},{"id":379,"data":1224,"type":224,"tunes":1226},{"text":1225},"The third example is even more important.",{},{"id":384,"data":1228,"type":295,"tunes":1229},{"code":386},{},{"id":389,"data":1231,"type":224,"tunes":1233},{"text":1232},"The model's reasoning capability may be perfectly useful. The missing component is evidence.",{},{"id":394,"data":1235,"type":224,"tunes":1237},{"text":1236},"That distinction is fundamental.",{},{"id":399,"data":1239,"type":42,"tunes":1241},{"text":1240,"level":218},"Where the Example Stops Working",{},{"id":404,"data":1243,"type":224,"tunes":1245},{"text":1244},"The examples above make the decision appear binary: retrieve or do not retrieve.",{},{"id":409,"data":1247,"type":224,"tunes":1249},{"text":1248},"Real systems are more complicated. A question may contain several claims, some stable and some current. Retrieved documents may disagree. A retriever may return irrelevant information. The relevant information may exist but fail to rank highly enough. A document may be authoritative but outdated.",{},{"id":414,"data":1251,"type":224,"tunes":1253},{"text":1252},"Retrieval itself can also introduce incorrect context into an otherwise reasonable answer.",{},{"id":419,"data":1255,"type":224,"tunes":1257},{"text":1256},"This is why retrieval should not be treated as an automatic synonym for truth.",{},{"id":424,"data":1259,"type":224,"tunes":1261},{"text":1260},"Research on adaptive retrieval has increasingly moved away from the assumption that every query should receive the same retrieval strategy.",{},{"id":429,"data":1263,"type":224,"tunes":1265},{"text":1264},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa>, for example, explicitly explores retrieval on demand rather than indiscriminately retrieving a fixed number of passages for every input. The authors discuss how unnecessary or irrelevant retrieval can reduce answer quality.",{},{"id":434,"data":1267,"type":224,"tunes":1269},{"text":1268},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> similarly selects between no retrieval, single-step retrieval and more complex retrieval strategies according to question complexity.",{},{"id":439,"data":1271,"type":224,"tunes":1273},{"text":1272},"So the important question is not: Does this system have RAG?",{},{"id":444,"data":1275,"type":224,"tunes":1277},{"text":1276},"It is: Can this system recognize when retrieval is necessary and what kind of retrieval is appropriate?",{},{"id":449,"data":1279,"type":42,"tunes":1281},{"text":1280,"level":218},"Direct Answer",{},{"id":454,"data":1283,"type":224,"tunes":1285},{"text":1284},"An AI should trigger retrieval when answering requires information that its internal model knowledge cannot safely provide with the required freshness, specificity, provenance or evidential support.",{},{"id":459,"data":1287,"type":224,"tunes":1289},{"text":1288},"In practical systems, a Retrieval Trigger can emerge from several conditions:",{},{"id":464,"data":1291,"type":295,"tunes":1292},{"code":466},{},{"id":469,"data":1294,"type":224,"tunes":1296},{"text":1295},"If none of these conditions is materially present, retrieval may be unnecessary. If one or more are present, external evidence becomes part of the answer-generation process.",{},{"id":474,"data":1298,"type":42,"tunes":1300},{"text":1299,"level":218},"Why This Is So",{},{"id":479,"data":1302,"type":224,"tunes":1304},{"text":1303},"A language model's internal knowledge is often described as parametric knowledge. It was learned during training and encoded into the model's parameters.",{},{"id":484,"data":1306,"type":224,"tunes":1308},{"text":1307},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">Lewis et al.'s original RAG work\u003C\u002Fa> framed retrieval as a combination of this parametric memory with external, non-parametric memory. The external memory can be searched and updated without retraining the entire language model.",{},{"id":489,"data":1310,"type":224,"tunes":1312},{"text":1311},"This distinction creates an unavoidable systems problem.",{},{"id":494,"data":1314,"type":224,"tunes":1316},{"text":1315},"The model can know things. But the model cannot assume that everything it knows is current, complete, specific enough and supported by the required evidence.",{},{"id":499,"data":1318,"type":224,"tunes":1320},{"text":1319},"A model can therefore produce a linguistically convincing answer while still operating beyond the point where its internal knowledge is sufficient.",{},{"id":504,"data":1322,"type":224,"tunes":1324},{"text":1323},"That point is where a Retrieval Trigger becomes useful.",{},{"id":509,"data":1326,"type":42,"tunes":1328},{"text":1327,"level":218},"Context",{},{"id":514,"data":1330,"type":224,"tunes":1332},{"text":1331},"Traditional RAG often looks like this:",{},{"id":519,"data":1334,"type":295,"tunes":1335},{"code":521},{},{"id":524,"data":1337,"type":224,"tunes":1339},{"text":1338},"This architecture assumes retrieval before generation. That works well for many knowledge-intensive applications, but it can also perform unnecessary retrieval.",{},{"id":529,"data":1341,"type":224,"tunes":1343},{"text":1342},"More advanced approaches introduce an adaptive step:",{},{"id":534,"data":1345,"type":295,"tunes":1346},{"code":536},{},{"id":539,"data":1348,"type":224,"tunes":1350},{"text":1349},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> goes further by considering retrieval during generation itself. It uses upcoming generation and low-confidence tokens as signals for retrieving additional information.",{},{"id":544,"data":1352,"type":224,"tunes":1354},{"text":1353},"Self-RAG similarly introduces mechanisms allowing retrieval, generation and critique to interact instead of treating retrieval as an unconditional preprocessing step.",{},{"id":549,"data":1356,"type":224,"tunes":1358},{"text":1357},"Adaptive-RAG approaches the same broader problem from query complexity: different questions may require different retrieval strategies.",{},{"id":554,"data":1360,"type":224,"tunes":1362},{"text":1361},"These approaches differ technically. But they expose the same architectural insight: Retrieval should be a decision, not merely a permanent switch.",{},{"id":559,"data":1364,"type":42,"tunes":1366},{"text":1365,"level":218},"Assumptions",{},{"id":564,"data":1368,"type":224,"tunes":1370},{"text":1369},"The Retrieval Trigger framework assumes that a system has access to at least one external information source when retrieval is required.",{},{"id":569,"data":1372,"type":224,"tunes":1374},{"text":1373},"That source could be web search, a document store, vector database, SQL database, knowledge graph, API, enterprise system, user-uploaded document or tool output.",{},{"id":574,"data":1376,"type":224,"tunes":1378},{"text":1377},"It also assumes that retrieval has a cost. That cost does not have to be financial.",{},{"id":579,"data":1380,"type":224,"tunes":1382},{"text":1381},"Retrieval introduces latency, token consumption, context usage, infrastructure complexity and the possibility of retrieving misleading information.",{},{"id":584,"data":1384,"type":224,"tunes":1386},{"text":1385},"The optimal system therefore does not maximize retrieval. It maximizes appropriate retrieval.",{},{"id":589,"data":1388,"type":42,"tunes":1389},{"text":591,"level":218},{},{"id":594,"data":1391,"type":224,"tunes":1393},{"text":1392},"A practical Retrieval Trigger can consider five primary variables.",{},{"id":599,"data":1395,"type":42,"tunes":1397},{"text":1396,"level":263},"Freshness",{},{"id":604,"data":1399,"type":224,"tunes":1401},{"text":1400},"How likely is the required information to have changed? The capital of France has very low volatility. A stock price has extremely high volatility.",{},{"id":609,"data":1403,"type":42,"tunes":1405},{"text":1404,"level":263},"Specificity",{},{"id":614,"data":1407,"type":224,"tunes":1409},{"text":1408},"Does the question require information from a particular source, document, organization, account or dataset? If the user asks what a specific contract says, general model knowledge is irrelevant. The contract must be retrieved.",{},{"id":619,"data":1411,"type":42,"tunes":1413},{"text":1412,"level":263},"Evidence Requirement",{},{"id":624,"data":1415,"type":224,"tunes":1417},{"text":1416},"Does the answer need provenance? A model may know that a claim is generally accepted but still need a source when the task requires verification.",{},{"id":629,"data":1419,"type":42,"tunes":1421},{"text":1420,"level":263},"Knowledge Coverage",{},{"id":634,"data":1423,"type":224,"tunes":1425},{"text":1424},"Is the subject likely to be represented adequately in internal model knowledge? Rare, proprietary, highly local or newly published information creates stronger retrieval pressure.",{},{"id":639,"data":1427,"type":42,"tunes":1429},{"text":1428,"level":263},"Consequence of Error",{},{"id":644,"data":1431,"type":224,"tunes":1433},{"text":1432},"Not every incorrect answer has the same impact. Where factual accuracy materially affects a decision, the acceptable evidence threshold may be higher.",{},{"id":649,"data":1435,"type":224,"tunes":1437},{"text":1436},"These variables do not have to be implemented as literal numeric scores. They describe the decision surface.",{},{"id":654,"data":1439,"type":42,"tunes":1441},{"text":1440,"level":218},"Diagnostic \u002F Decision Method",{},{"id":659,"data":1443,"type":224,"tunes":1445},{"text":1444},"A very simple Retrieval Trigger can be implemented without machine learning.",{},{"id":664,"data":1447,"type":295,"tunes":1448},{"code":666},{},{"id":669,"data":1450,"type":224,"tunes":1452},{"text":1451},"For a stable factual question:",{},{"id":674,"data":1454,"type":295,"tunes":1455},{"code":676},{},{"id":679,"data":1457,"type":224,"tunes":1459},{"text":1458},"For a current stock price:",{},{"id":684,"data":1461,"type":295,"tunes":1462},{"code":686},{},{"id":689,"data":1464,"type":224,"tunes":1466},{"text":1465},"For a scientific claim:",{},{"id":694,"data":1468,"type":295,"tunes":1469},{"code":696},{},{"id":699,"data":1471,"type":224,"tunes":1473},{"text":1472},"Production systems can make this decision far more sophisticated. A classifier could predict retrieval requirements. A model could emit special control tokens. A router could classify query complexity. Retrieval could also be triggered repeatedly during generation.",{},{"id":704,"data":1475,"type":224,"tunes":1477},{"text":1476},"The implementation can change. The architectural question remains the same:",{},{"id":709,"data":1479,"type":257,"tunes":1481},{"text":1480,"caption":712,"alignment":256},"Is the evidence currently available to the model sufficient for the answer it is about to produce?",{},{"id":715,"data":1483,"type":42,"tunes":1485},{"text":1484,"level":218},"Evidence",{},{"id":720,"data":1487,"type":224,"tunes":1489},{"text":1488},"The concept proposed here is consistent with several lines of retrieval research.",{},{"id":725,"data":1491,"type":224,"tunes":1493},{"text":1492},"The original \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">RAG architecture\u003C\u002Fa> demonstrated the usefulness of combining parametric model knowledge with external non-parametric knowledge, particularly for knowledge-intensive tasks.",{},{"id":730,"data":1495,"type":224,"tunes":1497},{"text":1496},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> explicitly explores active retrieval during generation, including retrieval prompted by low-confidence upcoming content.",{},{"id":735,"data":1499,"type":224,"tunes":1501},{"text":1500},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa> demonstrates an architecture in which retrieval can occur on demand and is followed by reflection on retrieved passages and generated content.",{},{"id":740,"data":1503,"type":224,"tunes":1505},{"text":1504},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> dynamically chooses among different strategies according to question complexity, including situations where no retrieval is required.",{},{"id":745,"data":1507,"type":224,"tunes":1509},{"text":1508},"The term Retrieval Trigger is used here as a system-level abstraction over this broader family of decisions.",{},{"id":750,"data":1511,"type":224,"tunes":1513},{"text":1512},"It does not claim that these papers use the same terminology. Instead, it identifies the shared architectural problem: What causes an AI system to transition from internal knowledge to external evidence?",{},{"id":755,"data":1515,"type":42,"tunes":1517},{"text":1516,"level":218},"Real Examples",{},{"id":760,"data":1519,"type":224,"tunes":1521},{"text":1520},"Consider a support assistant connected to a company's documentation.",{},{"id":765,"data":1523,"type":295,"tunes":1524},{"code":767},{},{"id":770,"data":1526,"type":224,"tunes":1528},{"text":1527},"If the procedure is stable and reliably represented in the assistant's current instructions, direct answering may be appropriate.",{},{"id":775,"data":1530,"type":295,"tunes":1531},{"code":777},{},{"id":780,"data":1533,"type":224,"tunes":1535},{"text":1534},"That information is user-specific and dynamic. The Retrieval Trigger fires. The system must inspect the actual account or authorization data.",{},{"id":785,"data":1537,"type":295,"tunes":1538},{"code":787},{},{"id":790,"data":1540,"type":224,"tunes":1542},{"text":1541},"The model can understand deployment systems and explain common reasons. But the question is asking about a particular event. Logs, CI\u002FCD output or incident records are required.",{},{"id":795,"data":1544,"type":224,"tunes":1546},{"text":1545},"The same logic works for web search.",{},{"id":800,"data":1548,"type":295,"tunes":1549},{"code":802},{},{"id":805,"data":1551,"type":224,"tunes":1553},{"text":1552},"A general explanation may not require retrieval.",{},{"id":810,"data":1555,"type":295,"tunes":1556},{"code":812},{},{"id":815,"data":1558,"type":224,"tunes":1560},{"text":1559},"Now source-specific evidence is required.",{},{"id":820,"data":1562,"type":295,"tunes":1563},{"code":822},{},{"id":825,"data":1565,"type":224,"tunes":1567},{"text":1566},"This introduces a freshness requirement as well. The underlying subject has not changed. The information requirement has.",{},{"id":830,"data":1569,"type":42,"tunes":1571},{"text":1570,"level":218},"Common Misconceptions and Failure Modes",{},{"id":835,"data":1573,"type":224,"tunes":1575},{"text":1574},"More retrieval automatically produces a better answer. It does not. Irrelevant documents consume context and can distract generation.",{},{"id":840,"data":1577,"type":224,"tunes":1579},{"text":1578},"High model confidence means retrieval is unnecessary. A model can produce an incorrect answer confidently. Self-reported confidence should therefore not be treated as the only trigger.",{},{"id":845,"data":1581,"type":224,"tunes":1583},{"text":1582},"Successful retrieval means the answer is verified. Retrieval only provides candidate evidence. The evidence must still be relevant, sufficiently authoritative and correctly interpreted.",{},{"id":850,"data":1585,"type":224,"tunes":1587},{"text":1586},"RAG automatically solves outdated knowledge. It only does so if the retrieval corpus itself contains current information. Retrieving an outdated document does not create a current answer.",{},{"id":855,"data":1589,"type":224,"tunes":1591},{"text":1590},"One retrieval step is always enough. Complex questions may require several pieces of evidence or iterative retrieval.",{},{"id":860,"data":1593,"type":42,"tunes":1595},{"text":1594,"level":218},"Edge Cases",{},{"id":865,"data":1597,"type":224,"tunes":1599},{"text":1598},"Some questions contain both stable and unstable information.",{},{"id":870,"data":1601,"type":295,"tunes":1602},{"code":872},{},{"id":875,"data":1604,"type":224,"tunes":1606},{"text":1605},"The first part may be answerable from stable model knowledge. The second part requires current information.",{},{"id":880,"data":1608,"type":224,"tunes":1610},{"text":1609},"A sufficiently capable system should not necessarily treat the entire query as one retrieval decision. It can trigger retrieval only where required.",{},{"id":885,"data":1612,"type":224,"tunes":1614},{"text":1613},"Another edge case is disagreement between sources. Suppose retrieval returns three documents making incompatible claims.",{},{"id":890,"data":1616,"type":224,"tunes":1618},{"text":1617},"The Retrieval Trigger has already succeeded: the system recognized that external evidence was required. But the task is not finished.",{},{"id":895,"data":1620,"type":224,"tunes":1622},{"text":1621},"The system has now reached an evidence evaluation problem. This is where the Answer Validity Boundary becomes important.",{},{"id":900,"data":1624,"type":224,"tunes":1626},{"text":1625},"The system may have retrieved information and still not possess enough evidence to make a strong conclusion.",{},{"id":905,"data":1628,"type":295,"tunes":1629},{"code":907},{},{"id":910,"data":1631,"type":224,"tunes":1633},{"text":1632},"The trigger obtains evidence. The validity boundary determines whether that evidence is sufficient.",{},{"id":915,"data":1635,"type":42,"tunes":1637},{"text":1636,"level":218},"Limitations",{},{"id":920,"data":1639,"type":224,"tunes":1641},{"text":1640},"The Retrieval Trigger is a conceptual framework, not a universal algorithm.",{},{"id":925,"data":1643,"type":224,"tunes":1645},{"text":1644},"Different systems will require different trigger rules. A customer-support bot, scientific research assistant, search engine and autonomous software agent do not have identical evidence requirements.",{},{"id":930,"data":1647,"type":224,"tunes":1649},{"text":1648},"Trigger thresholds can also create their own failure modes. A threshold that is too low causes excessive retrieval. A threshold that is too high causes unsupported answering.",{},{"id":935,"data":1651,"type":224,"tunes":1653},{"text":1652},"The retrieval infrastructure itself also matters. A perfect trigger connected to a poor source collection still produces poor evidence.",{},{"id":940,"data":1655,"type":224,"tunes":1657},{"text":1656},"Similarly, an excellent knowledge base provides little value if the trigger never activates when it is needed.",{},{"id":945,"data":1659,"type":224,"tunes":1661},{"text":1660},"The Retrieval Trigger therefore solves only one part of a larger architecture.",{},{"id":950,"data":1663,"type":42,"tunes":1665},{"text":1664,"level":218},"What Would Change This Answer?",{},{"id":955,"data":1667,"type":224,"tunes":1669},{"text":1668},"Future models may contain better mechanisms for identifying their own knowledge limitations. Retrievers may become cheaper and faster. Long-context systems may carry far more source material continuously.",{},{"id":960,"data":1671,"type":224,"tunes":1673},{"text":1672},"Models may also increasingly combine search, databases, tools and structured knowledge without exposing a distinct RAG stage to the application developer.",{},{"id":965,"data":1675,"type":224,"tunes":1677},{"text":1676},"These changes could alter how the trigger is implemented. They do not necessarily remove the underlying decision.",{},{"id":970,"data":1679,"type":224,"tunes":1681},{"text":1680},"As long as there is a difference between information already available to the model and information that must be obtained externally, a system still needs some mechanism for determining when to cross that boundary.",{},{"id":975,"data":1683,"type":224,"tunes":1685},{"text":1684},"The implementation may disappear from view. The architectural question remains.",{},{"id":980,"data":1687,"type":42,"tunes":1689},{"text":1688,"level":218},"Conclusion",{},{"id":985,"data":1691,"type":224,"tunes":1693},{"text":1692},"RAG begins too late to explain the whole problem.",{},{"id":990,"data":1695,"type":224,"tunes":1697},{"text":1696},"Before retrieval can happen, an AI system must determine whether retrieval is necessary. That decision is the Retrieval Trigger.",{},{"id":995,"data":1699,"type":295,"tunes":1700},{"code":997},{},{"id":1000,"data":1702,"type":224,"tunes":1704},{"text":1703},"But the broader implication is more important. Reliable AI does not merely need access to knowledge. It needs a method for determining when its current knowledge is insufficient.",{},{"id":1005,"data":1706,"type":295,"tunes":1707},{"code":1007},{},{"id":1010,"data":1709,"type":224,"tunes":1711},{"text":1710},"The Retrieval Trigger determines when the system should seek evidence. The Answer Validity Boundary determines whether that evidence is sufficient.",{},{"id":1015,"data":1713,"type":224,"tunes":1715},{"text":1714},"Together they describe something more useful than RAG alone: a decision process for moving from what an AI appears to know toward what it can actually support.",{},{"id":1020,"data":1717,"type":42,"tunes":1719},{"text":1718,"level":218},"Primary Sources",{},{"id":1025,"data":1721,"type":224,"tunes":1723},{"text":1722},"Patrick Lewis et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\u003C\u002Fa> (2020). Foundational RAG work describing the combination of parametric model memory with external non-parametric memory.",{},{"id":1030,"data":1725,"type":224,"tunes":1727},{"text":1726},"Zhengbao Jiang et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">Active Retrieval Augmented Generation\u003C\u002Fa> (2023). Introduces FLARE and active retrieval during generation, including retrieval based on low-confidence predicted content.",{},{"id":1035,"data":1729,"type":224,"tunes":1731},{"text":1730},"Akari Asai et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection\u003C\u002Fa> (2023). Explores adaptive retrieval on demand and self-reflection instead of unconditional fixed retrieval.",{},{"id":1040,"data":1733,"type":224,"tunes":1735},{"text":1734},"Soyeong Jeong et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity\u003C\u002Fa> (2024). Dynamically selects among no retrieval, single-step retrieval and more complex retrieval strategies according to the incoming question.",{},"2.31.6","An AI model does not need retrieval for every question. The important problem is knowing when its internal knowledge is no longer enough. The Retrieval Trigger is a practical decision boundary that determines when an AI system should stop relying solely on model knowledge and obtain external evidence before 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Este artículo proporciona un modelo práctico de ciclo de vida para decidir qué pertenece a la memoria duradera, qué se debería recuperar de nuevo, qué es más seguro recalcular y qué debería expirar o ser sustituido.","\u002Fuploads\u002F2026\u002F09\u002Fwhat-should-an-ai-agent-remember-forget-recompute-or-retrieve-again-1790351131087-iehz28.webp","2026-09-25T09:43:00.000Z",{"id":2259,"slug":2260,"title":2261,"excerpt":2262,"featuredImage":2263,"publishedAt":2264},"479","where-does-an-llm-get-its-data-rag-data-sources-in-python","¿De dónde obtiene sus datos un LLM? Fuentes de datos RAG en Python","Un LLM no conoce mágicamente tus archivos, bases de datos o APIs. 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Este modelo práctico de arquitectura separa las cuatro capas, muestra dónde pertenece cada una y explica qué se rompe cuando los sistemas las colapsan en una sola.","\u002Fuploads\u002F2026\u002F09\u002Fai-agent-memory-is-not-rag-how-to-separate-memory-retrieval-state-and-context-1790350560308-np0xy6.webp","2026-09-25T11:34:00.000Z",{"id":2294,"slug":2295,"title":2296,"excerpt":2297,"featuredImage":2298,"publishedAt":2299},"459","ollama-is-not-the-product-building-production-ready-open-llm-applications","Ollama no es el producto: Construcción de aplicaciones de LLM abiertos listas para producción","Ejecutar un modelo local con Ollama es fácil. 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