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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":1798},{"id":207,"title":208,"slug":209,"content":210,"contentJson":211,"excerpt":874,"featuredImage":875,"featuredImageAlt":876,"featuredImageCaption":10,"featuredImageTitle":10,"featuredImageCopyright":10,"featuredImageAuthor":10,"featuredImageSourceUrl":10,"featuredImageLicense":10,"featuredImageIsAiGenerated":43,"status":877,"publishedAt":878,"createdAt":879,"updatedAt":880,"seoLocalePaths":881,"categories":890,"author":915,"translations":920},"478","¿Qué es RAG? La explicación más sencilla de cómo funciona","what-is-rag-the-simplest-explanation-of-how-it-works","\u003Cp>RAG suena complicado porque el nombre es complicado. La idea no lo es. RAG simplemente significa: antes de que la IA responda, primero busca información relevante de una fuente de conocimiento y le da esa información al modelo de lenguaje.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--info my-6 rounded-xl border p-5 border-blue-300 bg-blue-50 dark:border-blue-900 dark:bg-blue-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">RAG en una frase\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>RAG es el paso donde una IA busca en una base de conocimiento información útil antes de que el LLM escriba la respuesta.\u003C\u002Fstrong>\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cp>Piensa en un LLM como una persona inteligente sentada en un escritorio. RAG es el bibliotecario que trae la página correcta del libro correcto. Luego el LLM lee esa página y te responde.\u003C\u002Fp>\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-5\" class=\"editorjs-toc__link\">Primero: ¿qué hace el LLM?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-10\" class=\"editorjs-toc__link\">Luego: ¿qué es la base de conocimiento?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-15\" class=\"editorjs-toc__link\">Entonces, ¿qué hace RAG realmente?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-19\" class=\"editorjs-toc__link\">Un ejemplo muy sencillo\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-25\" class=\"editorjs-toc__link\">Ahora la parte importante: RAG no es el estado actual\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-30\" class=\"editorjs-toc__link\">¿Qué es una base de datos de estado?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-36\" class=\"editorjs-toc__link\">Cómo funcionan juntas las tres piezas\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-40\" class=\"editorjs-toc__link\">¿RAG siempre usa una base de datos vectorial?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-45\" class=\"editorjs-toc__link\">¿Qué es un embedding, en lenguaje sencillo?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-50\" class=\"editorjs-toc__link\">RAG tampoco es memoria\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-54\" class=\"editorjs-toc__link\">Un ejemplo real de juego: PUBG Ally\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-60\" class=\"editorjs-toc__link\">Un ejemplo completo\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-64\" class=\"editorjs-toc__link\">¿Por qué usar RAG en absoluto?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-68\" class=\"editorjs-toc__link\">Lo que RAG no garantiza\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-72\" class=\"editorjs-toc__link\">El modelo mental más fácil de recordar\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-75\" class=\"editorjs-toc__link\">Conclusión\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-79\" class=\"editorjs-toc__link\">Preguntas frecuentes\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-81\" class=\"editorjs-toc__link\">Glosario\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-83\" class=\"editorjs-toc__link\">Fuentes primarias\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">Primero: ¿qué hace el LLM?\u003C\u002Fh2>\n\u003Cp>El LLM es la parte que entiende el lenguaje y produce lenguaje. Puede leer tu pregunta, entender instrucciones, comparar información, explicar algo y escribir una respuesta.\u003C\u002Fp>\n\u003Cp>Pero el LLM no sabe automáticamente qué hay actualmente dentro de la base de datos de tu empresa, tu sesión de juego, tus documentos privados o un archivo que creaste hace cinco minutos.\u003C\u002Fp>\n\u003Cp>Solo sabe lo que ya está dentro del modelo más cualquier información que la aplicación le dé en la solicitud actual.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--note my-6 rounded-xl border p-5 border-gray-300 bg-gray-50 dark:border-gray-700 dark:bg-gray-900\u002F40\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Regla simple\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">El LLM \u003Cstrong>piensa y escribe\u003C\u002Fstrong>. No posee automáticamente todos tus datos actuales.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-10\">Luego: ¿qué es la base de conocimiento?\u003C\u002Fh2>\n\u003Cp>Una base de conocimiento es simplemente información que la aplicación puede buscar.\u003C\u002Fp>\n\u003Cp>Podría contener PDFs, manuales, documentación de productos, artículos de soporte, contratos, reglas de juegos, datos de armas, documentos internos de la empresa, registros de bases de datos u otro texto.\u003C\u002Fp>\n\u003Cp>La base de conocimiento puede ser local en tu propia máquina. Puede estar en un servidor. Puede estar en una base de datos vectorial. También puede construirse a partir de archivos normales. RAG no significa Internet.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--success my-6 rounded-xl border p-5 border-emerald-300 bg-emerald-50 dark:border-emerald-900 dark:bg-emerald-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Importante\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>RAG no requiere Internet.\u003C\u002Fstrong> La información puede ser completamente local.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-15\">Entonces, ¿qué hace RAG realmente?\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Todo el proceso RAG\u003C\u002Fh3>\u003Cdiv class=\"flex flex-col sm:flex-row gap-3\">\u003Cdiv class=\"editorjs-process__step min-w-0 flex-1 rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">1. Haces una pregunta\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Por ejemplo: ¿Qué munición usa esta arma?\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90 sm:rotate-0\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0 flex-1 rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">2. RAG busca en la base de conocimiento\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El sistema busca los pequeños fragmentos de información más relevantes para tu pregunta.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90 sm:rotate-0\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0 flex-1 rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">3. RAG le da esos fragmentos al LLM\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El LLM recibe la pregunta más la información recuperada.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90 sm:rotate-0\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0 flex-1 rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">4. El LLM escribe la respuesta\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Utiliza la información recuperada como contexto para la respuesta.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Cp>Eso es RAG.\u003C\u002Fp>\n\u003Cp>El nombre completo es Generación Aumentada por Recuperación. Recuperación significa encontrar la información relevante. Aumentada significa añadir esa información al contexto del modelo. Generación significa que el LLM escribe la respuesta final.\u003C\u002Fp>\n\u003Ch2 id=\"section-19\">Un ejemplo muy sencillo\u003C\u002Fh2>\n\u003Cp>Imagina que tienes una base de conocimiento local sobre un juego.\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\">La base de conocimiento contiene\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Ejemplo\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Armas\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">El AKM usa munición de 7.62 mm\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Objetos de curación\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">El botiquín restaura salud\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Accesorios\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Este accesorio funciona con estas armas\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Reglas del mapa\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Esta zona se comporta de esta manera\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Cp>Preguntas: “¿Qué munición usa el AKM?”\u003C\u002Fp>\n\u003Cp>RAG busca en la base de conocimiento y encuentra la entrada sobre el AKM. Le da esa pequeña pieza de información al LLM. El LLM entonces responde: “El AKM usa munición de 7.62 mm.”\u003C\u002Fp>\n\u003Cp>El LLM no necesitaba toda la base de datos. RAG solo trajo la parte útil.\u003C\u002Fp>\n\u003Ch2 id=\"section-25\">Ahora la parte importante: RAG no es el estado actual\u003C\u002Fh2>\n\u003Cp>Aquí es donde muchas explicaciones se vuelven confusas.\u003C\u002Fp>\n\u003Cp>RAG normalmente le da conocimiento a la IA. Un sistema de estado le da a la IA hechos sobre lo que es verdad en este momento.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Conocimiento vs estado actual\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">RAG \u002F conocimiento\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Estado actual\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Arma\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Munición\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Salud\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Enemigo\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-callout editorjs-callout--warning my-6 rounded-xl border p-5 border-amber-300 bg-amber-50 dark:border-amber-900 dark:bg-amber-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">No mezcles estos dos\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">RAG responde: \u003Cstrong>¿Qué es generalmente verdad?\u003C\u002Fstrong>\u003Cbr>El estado responde: \u003Cstrong>¿Qué es verdad en este momento?\u003C\u002Fstrong>\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-30\">¿Qué es una base de datos de estado?\u003C\u002Fh2>\n\u003Cp>Una base de datos de estado o almacén de estado es simplemente un lugar donde la aplicación guarda hechos actuales.\u003C\u002Fp>\n\u003Cp>En un juego, el motor ya sabe cosas como tu salud, posición, inventario, munición, misión actual, objetos cercanos y estado de los enemigos. Un sistema de IA puede exponer partes seleccionadas de ese estado al modelo.\u003C\u002Fp>\n\u003Cp>En una aplicación empresarial, la misma idea podría ser una base de datos de pedidos, un registro de cliente, un estado de proyecto o el valor actual de un sensor.\u003C\u002Fp>\n\u003Cp>El estado lo crea la propia aplicación a medida que ocurren las cosas. Si pierdes salud, el juego actualiza el valor de salud. Si recoges munición, el inventario cambia. Si se paga un pedido, el sistema empresarial cambia el estado del pedido.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--info my-6 rounded-xl border p-5 border-blue-300 bg-blue-50 dark:border-blue-900 dark:bg-blue-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Regla simple\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">La aplicación crea y actualiza el \u003Cstrong>estado\u003C\u002Fstrong>. RAG busca \u003Cstrong>conocimiento\u003C\u002Fstrong>. El LLM usa ambos para decidir qué decir o hacer.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-36\">Cómo funcionan juntas las tres piezas\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">LLM + estado + RAG\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">1. Estado actual\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">La aplicación le dice a la IA qué es verdad ahora: salud 41%, AKM equipada, 23 balas.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">2. RAG\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El sistema recupera conocimiento útil: cómo funciona el arma, qué objeto de curación está disponible o una regla relevante.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">3. LLM\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El modelo recibe la pregunta, el estado actual y el conocimiento recuperado.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">4. Razonamiento\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El LLM combina esas entradas y decide qué respuesta o acción de alto nivel tiene sentido.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">5. Aplicación\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Si se requiere una acción, la aplicación o el motor del juego la ejecuta y actualiza el estado de nuevo.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Cp>Entonces la arquitectura básica es:\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--note my-6 rounded-xl border p-5 border-gray-300 bg-gray-50 dark:border-gray-700 dark:bg-gray-900\u002F40\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">La arquitectura más simple\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>Estado = lo que es verdad ahora\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>RAG = conocimiento útil\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>LLM = entiende, razona y escribe\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Aplicación = realiza la acción real\u003C\u002Fstrong>\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-40\">¿RAG siempre usa una base de datos vectorial?\u003C\u002Fh2>\n\u003Cp>No.\u003C\u002Fp>\n\u003Cp>Una base de datos vectorial es una forma común de construir búsqueda semántica, pero no es la definición de RAG.\u003C\u002Fp>\n\u003Cp>La parte importante es la recuperación: el sistema encuentra información externa relevante y la añade al contexto del LLM antes de que se genere la respuesta.\u003C\u002Fp>\n\u003Cp>La Búsqueda de Archivos de OpenAI, por ejemplo, puede trabajar con archivos almacenados en almacenes vectoriales. Los archivos se dividen en fragmentos más pequeños para que el sistema pueda recuperar las partes relevantes para una pregunta. Esa es una implementación de la misma idea básica.\u003C\u002Fp>\n\u003Ch2 id=\"section-45\">¿Qué es un embedding, en lenguaje sencillo?\u003C\u002Fh2>\n\u003Cp>No necesitas entender los embeddings para entender RAG.\u003C\u002Fp>\n\u003Cp>Pero la versión simple es esta: un embedding es una representación numérica del significado. Ayuda a un sistema de búsqueda a encontrar texto que es conceptualmente similar incluso cuando las palabras no son exactamente las mismas.\u003C\u002Fp>\n\u003Cp>Por ejemplo, una búsqueda normal por palabras clave puede buscar las palabras exactas \"reparación de coche\". La búsqueda semántica también puede entender que \"arreglar mi vehículo\" trata sobre un tema similar.\u003C\u002Fp>\n\u003Cp>Eso hace que los embeddings sean útiles para RAG, pero RAG también puede usar búsqueda por palabras clave, consultas a bases de datos o una combinación de varios métodos.\u003C\u002Fp>\n\u003Ch2 id=\"section-50\">RAG tampoco es memoria\u003C\u002Fh2>\n\u003Cp>La memoria es otro concepto que a menudo se mezcla con RAG.\u003C\u002Fp>\n\u003Cp>La memoria suele ser información que el sistema guarda sobre interacciones previas o eventos previos. RAG es el mecanismo utilizado para recuperar conocimiento relevante cuando se necesita.\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\">Parte\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Significado simple\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">LLM\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">La parte que entiende y genera lenguaje\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">RAG\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">La parte que busca conocimiento relevante antes de la respuesta\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Base de conocimiento\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">La información que RAG puede buscar\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Estado\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Lo que es verdad ahora mismo en la aplicación o el mundo\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Memoria\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Información conservada de interacciones o eventos previos\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Herramienta \u002F acción\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Algo que la IA tiene permitido llamar o pedir a la aplicación que haga\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Contexto\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">La información colocada actualmente frente al LLM para esta solicitud\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-54\">Un ejemplo real de juego: PUBG Ally\u003C\u002Fh2>\n\u003Cp>PUBG Ally es un ejemplo útil porque hace visible la diferencia.\u003C\u002Fp>\n\u003Cp>KRAFTON describe el estado de la partida en vivo como una fuente de verdad separada. El juego expone los hechos actuales a través de herramientas de observación: arma actual, munición, salud, estado de la zona segura, objetos cercanos y situación de combate.\u003C\u002Fp>\n\u003Cp>La consulta de conocimiento es una tarea diferente. El sistema puede usar conocimiento curado sobre armas, accesorios, objetos y reglas. El SDK ACE Game Agent de NVIDIA también expone una API RAG separada para recuperar conocimiento de bases de datos creadas por desarrolladores.\u003C\u002Fp>\n\u003Cp>Eso nos da la separación clara: el motor del juego dice qué está pasando ahora, la recuperación proporciona conocimiento relevante y el modelo de lenguaje decide qué significa la información.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">PUBG Ally muestra por qué los compañeros de equipo de IA necesitan dos cerebros: reflejos rápidos y razonamiento lento\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Un ejemplo práctico de juego que muestra cómo el estado en vivo, el razonamiento del lenguaje y el control determinista del lado del juego pueden funcionar juntos.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Leer el artículo sobre la arquitectura de PUBG Ally →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-60\">Un ejemplo completo\u003C\u002Fh2>\n\u003Cp>Imagina que le dices a un compañero de equipo de IA: “Tengo poca salud. ¿Deberíamos atacar?”\u003C\u002Fp>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Qué sucede después\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Estado\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El juego informa: salud 24%, un enemigo cerca, dos objetos de curación disponibles.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">RAG\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El sistema de conocimiento recupera las reglas relevantes para el objeto de curación y quizás información sobre el arma actual o la mecánica táctica.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">LLM\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El modelo combina tu solicitud, el estado actual y el conocimiento recuperado.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Decisión\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Concluye que curarse primero es más seguro que atacar de inmediato.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Herramienta \u002F motor del juego\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El agente solicita una acción de juego legal, como moverse a cubierto o usar el objeto de curación.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">6\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Nuevo estado\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El juego ejecuta la acción e informa la situación actualizada de vuelta al agente.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Cp>RAG no controló al personaje. La base de datos de estado no razonó. El LLM no cambió directamente el juego. Cada parte tenía una tarea.\u003C\u002Fp>\n\u003Ch2 id=\"section-64\">¿Por qué usar RAG en absoluto?\u003C\u002Fh2>\n\u003Cp>Porque poner cada documento, regla y registro de base de datos en cada prompt sería lento, costoso y a menudo confuso.\u003C\u002Fp>\n\u003Cp>RAG permite al sistema seleccionar solo la información que es útil para la pregunta actual.\u003C\u002Fp>\n\u003Cp>También te permite actualizar la base de conocimiento sin reentrenar todo el modelo de lenguaje. Cambia el documento o la base de datos, reconstruye o actualiza el índice cuando sea necesario, y la siguiente recuperación podrá usar la información más reciente.\u003C\u002Fp>\n\u003Ch2 id=\"section-68\">Lo que RAG no garantiza\u003C\u002Fh2>\n\u003Cp>RAG puede mejorar la fundamentación, pero no hace que una respuesta sea automáticamente correcta.\u003C\u002Fp>\n\u003Cp>El paso de recuperación puede encontrar el documento equivocado. El documento correcto puede estar desactualizado. El LLM puede malinterpretar una buena evidencia. O el estado actual puede haber cambiado.\u003C\u002Fp>\n\u003Cp>Por lo tanto, un sistema confiable tiene que validar por separado la recuperación, la frescura del estado y el razonamiento final del modelo.\u003C\u002Fp>\n\u003Ch2 id=\"section-72\">El modelo mental más fácil de recordar\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Piensa en un sistema de IA como una persona en un escritorio\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Analogía\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Sistema de IA\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Persona pensando\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Buscar un libro de referencia\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Libros en el estante\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Panel de control o panel de instrumentos actual\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Notas de reuniones anteriores\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Hacer algo en el mundo real\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-callout editorjs-callout--success my-6 rounded-xl border p-5 border-emerald-300 bg-emerald-50 dark:border-emerald-900 dark:bg-emerald-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Si solo recuerdas esto\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>LLM = cerebro.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>RAG = bibliotecario.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Base de conocimiento = biblioteca.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Estado = lo que dice el panel de control en este momento.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Herramientas = las manos que realmente pueden hacer algo.\u003C\u002Fstrong>\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-75\">Conclusión\u003C\u002Fh2>\n\u003Cp>RAG es mucho menos misterioso una vez que se separan las partes.\u003C\u002Fp>\n\u003Cp>El LLM entiende y genera lenguaje. La aplicación mantiene el estado actual. La base de conocimiento almacena información. RAG encuentra la parte útil de esa información y la coloca en el contexto del LLM. Las herramientas o la aplicación realizan acciones reales.\u003C\u002Fp>\n\u003Cp>Esa es la arquitectura básica detrás de muchos asistentes y agentes de IA modernos.\u003C\u002Fp>\n\u003Ch2 id=\"section-79\">Preguntas frecuentes\u003C\u002Fh2>\n\u003Csection class=\"editorjs-faq my-6 rounded-xl border border-gray-200 p-5 dark:border-gray-700\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">RAG en lenguaje sencillo\u003C\u002Fh3>\u003Cdiv id=\"faq1\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">¿Qué es RAG en términos simples?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">RAG es un paso en el que una IA busca información relevante en una fuente de conocimiento antes de que el modelo de lenguaje escriba su respuesta.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq2\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">¿RAG necesita Internet?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No. La base de conocimiento puede estar completamente local en tu computadora o servidor.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq3\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">¿RAG es lo mismo que una base de datos?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No. La base de datos o los archivos contienen la información. RAG es el proceso de recuperación que encuentra la parte útil y se la da al LLM.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq4\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">¿RAG es lo mismo que memoria?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No. La memoria generalmente almacena interacciones o eventos previos. RAG recupera conocimiento relevante cuando se necesita.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq5\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">¿El estado actual de la aplicación es parte de RAG?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No necesariamente. El estado actual generalmente se obtiene directamente de la aplicación o de un almacén de estado. RAG se entiende mejor como la recuperación desde una fuente de conocimiento.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq6\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">¿RAG hace que las respuestas de la IA sean correctas?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No. Puede proporcionar mejores evidencias, pero la recuperación aún puede ser incorrecta o estar desactualizada y el LLM aún puede razonar incorrectamente.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-81\">Glosario\u003C\u002Fh2>\n\u003Csection class=\"editorjs-glossary my-6 rounded-xl border border-gray-200 dark:border-gray-700 p-5\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Los términos básicos\u003C\u002Fh3>\u003Cdl>\u003Cdiv id=\"llm\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">LLM\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un modelo de lenguaje que entiende y genera texto y puede razonar sobre la información colocada en su contexto.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"rag\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">RAG\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Generación Aumentada por Recuperación: recuperar información externa relevante y agregarla al contexto del modelo antes de generar una respuesta.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"knowledge-base\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Base de conocimiento\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Los archivos, documentos, registros u otra información que la recuperación puede buscar.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"state\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Estado\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Los hechos actuales de una aplicación, sistema o mundo en un momento particular.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"context\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Contexto\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">La información proporcionada actualmente al modelo de lenguaje para una solicitud o paso de razonamiento.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"embedding\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Embedding\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Una representación numérica del significado que puede ayudar a la búsqueda semántica a encontrar información conceptualmente similar.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>\n\u003Ch2 id=\"section-83\">Fuentes primarias\u003C\u002Fh2>\n\u003Ca href=\"https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fapi-reference\u002Fvector-stores-files\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">OpenAI — Archivos de Vector Store\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Documentación oficial que muestra cómo se pueden adjuntar archivos a los almacenes vectoriales, dividirlos en fragmentos y ponerlos a disposición para la recuperación de búsqueda de archivos.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fquickstart\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">OpenAI — Guía de inicio rápido para desarrolladores\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Documentación oficial de OpenAI que describe herramientas como la búsqueda de archivos para dar a los modelos acceso a información externa.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdeveloper.nvidia.com\u002Face-for-games\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA Developer — ACE para juegos\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Documentación oficial de NVIDIA que describe API separadas de Agente, Chat y RAG para conectar personajes de juegos con el estado del juego, conocimiento contextual y acciones impulsadas por modelos.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fhow-krafton-built-pubg-ally-a-co-playable-character-powered-by-nvidia-ace\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA Developer — Cómo KRAFTON construyó PUBG Ally\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Explicación técnica oficial que separa el estado de la partida en vivo de la búsqueda de conocimiento y el razonamiento del modelo de lenguaje.\u003C\u002Fp>\u003C\u002Fa>",{"time":212,"blocks":213,"version":873},1790377565474,[214,220,228,233,241,246,251,256,261,268,273,278,283,288,295,300,320,325,330,335,340,361,366,371,376,381,386,391,422,429,434,439,444,449,454,459,464,485,490,496,501,506,511,516,521,526,531,536,541,546,551,556,561,590,595,600,605,610,615,624,629,634,655,660,665,670,675,680,685,690,695,700,705,741,747,752,757,762,767,772,802,807,831,836,846,855,864],{"id":215,"data":216,"type":218,"tunes":219},"intro",{"text":217},"RAG suena complicado porque el nombre es complicado. La idea no lo es. RAG simplemente significa: antes de que la IA responda, primero busca información relevante de una fuente de conocimiento y le da esa información al modelo de lenguaje.","paragraph",{},{"id":221,"data":222,"type":226,"tunes":227},"one-sentence",{"body":223,"title":224,"variant":225},"\u003Cstrong>RAG es el paso donde una IA busca en una base de conocimiento información útil antes de que el LLM escriba la respuesta.\u003C\u002Fstrong>","RAG en una frase","info","callout",{},{"id":229,"data":230,"type":218,"tunes":232},"analogy",{"text":231},"Piensa en un LLM como una persona inteligente sentada en un escritorio. RAG es el bibliotecario que trae la página correcta del libro correcto. Luego el LLM lee esa página y te responde.",{},{"id":234,"data":235,"type":239,"tunes":240},"toc",{"title":236,"maxLevel":237,"minLevel":238},"Contenido",3,2,"tableOfContents",{},{"id":242,"data":243,"type":42,"tunes":245},"h-llm",{"text":244,"level":238},"Primero: ¿qué hace el LLM?",{},{"id":247,"data":248,"type":218,"tunes":250},"p-llm-1",{"text":249},"El LLM es la parte que entiende el lenguaje y produce lenguaje. Puede leer tu pregunta, entender instrucciones, comparar información, explicar algo y escribir una respuesta.",{},{"id":252,"data":253,"type":218,"tunes":255},"p-llm-2",{"text":254},"Pero el LLM no sabe automáticamente qué hay actualmente dentro de la base de datos de tu empresa, tu sesión de juego, tus documentos privados o un archivo que creaste hace cinco minutos.",{},{"id":257,"data":258,"type":218,"tunes":260},"p-llm-3",{"text":259},"Solo sabe lo que ya está dentro del modelo más cualquier información que la aplicación le dé en la solicitud actual.",{},{"id":262,"data":263,"type":226,"tunes":267},"llm-rule",{"body":264,"title":265,"variant":266},"El LLM \u003Cstrong>piensa y escribe\u003C\u002Fstrong>. No posee automáticamente todos tus datos actuales.","Regla simple","note",{},{"id":269,"data":270,"type":42,"tunes":272},"h-kb",{"text":271,"level":238},"Luego: ¿qué es la base de conocimiento?",{},{"id":274,"data":275,"type":218,"tunes":277},"p-kb-1",{"text":276},"Una base de conocimiento es simplemente información que la aplicación puede buscar.",{},{"id":279,"data":280,"type":218,"tunes":282},"p-kb-2",{"text":281},"Podría contener PDFs, manuales, documentación de productos, artículos de soporte, contratos, reglas de juegos, datos de armas, documentos internos de la empresa, registros de bases de datos u otro texto.",{},{"id":284,"data":285,"type":218,"tunes":287},"p-kb-3",{"text":286},"La base de conocimiento puede ser local en tu propia máquina. Puede estar en un servidor. Puede estar en una base de datos vectorial. También puede construirse a partir de archivos normales. RAG no significa Internet.",{},{"id":289,"data":290,"type":226,"tunes":294},"no-internet",{"body":291,"title":292,"variant":293},"\u003Cstrong>RAG no requiere Internet.\u003C\u002Fstrong> La información puede ser completamente local.","Importante","success",{},{"id":296,"data":297,"type":42,"tunes":299},"h-rag",{"text":298,"level":238},"Entonces, ¿qué hace RAG realmente?",{},{"id":301,"data":302,"type":318,"tunes":319},"rag-flow",{"steps":303,"title":316,"orientation":317},[304,307,310,313],{"label":305,"description":306},"1. Haces una pregunta","Por ejemplo: ¿Qué munición usa esta arma?",{"label":308,"description":309},"2. RAG busca en la base de conocimiento","El sistema busca los pequeños fragmentos de información más relevantes para tu pregunta.",{"label":311,"description":312},"3. RAG le da esos fragmentos al LLM","El LLM recibe la pregunta más la información recuperada.",{"label":314,"description":315},"4. El LLM escribe la respuesta","Utiliza la información recuperada como contexto para la respuesta.","Todo el proceso RAG","auto","processFlow",{},{"id":321,"data":322,"type":218,"tunes":324},"rag-that-is-it",{"text":323},"Eso es RAG.",{},{"id":326,"data":327,"type":218,"tunes":329},"rag-name",{"text":328},"El nombre completo es Generación Aumentada por Recuperación. Recuperación significa encontrar la información relevante. Aumentada significa añadir esa información al contexto del modelo. Generación significa que el LLM escribe la respuesta final.",{},{"id":331,"data":332,"type":42,"tunes":334},"h-example",{"text":333,"level":238},"Un ejemplo muy sencillo",{},{"id":336,"data":337,"type":218,"tunes":339},"p-ex-1",{"text":338},"Imagina que tienes una base de conocimiento local sobre un juego.",{},{"id":341,"data":342,"type":359,"tunes":360},"kb-table",{"content":343,"stretched":43,"withHeadings":14},[344,347,350,353,356],[345,346],"La base de conocimiento contiene","Ejemplo",[348,349],"Armas","El AKM usa munición de 7.62 mm",[351,352],"Objetos de curación","El botiquín restaura salud",[354,355],"Accesorios","Este accesorio funciona con estas armas",[357,358],"Reglas del mapa","Esta zona se comporta de esta manera","table",{},{"id":362,"data":363,"type":218,"tunes":365},"p-ex-2",{"text":364},"Preguntas: “¿Qué munición usa el AKM?”",{},{"id":367,"data":368,"type":218,"tunes":370},"p-ex-3",{"text":369},"RAG busca en la base de conocimiento y encuentra la entrada sobre el AKM. Le da esa pequeña pieza de información al LLM. El LLM entonces responde: “El AKM usa munición de 7.62 mm.”",{},{"id":372,"data":373,"type":218,"tunes":375},"p-ex-4",{"text":374},"El LLM no necesitaba toda la base de datos. RAG solo trajo la parte útil.",{},{"id":377,"data":378,"type":42,"tunes":380},"h-state",{"text":379,"level":238},"Ahora la parte importante: RAG no es el estado actual",{},{"id":382,"data":383,"type":218,"tunes":385},"p-state-1",{"text":384},"Aquí es donde muchas explicaciones se vuelven confusas.",{},{"id":387,"data":388,"type":218,"tunes":390},"p-state-2",{"text":389},"RAG normalmente le da conocimiento a la IA. Un sistema de estado le da a la IA hechos sobre lo que es verdad en este momento.",{},{"id":392,"data":393,"type":420,"tunes":421},"knowledge-state",{"rows":394,"title":412,"layout":359,"columns":413},[395,400,404,408],{"id":396,"label":397,"values":398},"weapon","Arma",[399,399],"",{"id":401,"label":402,"values":403},"ammo","Munición",[399,399],{"id":405,"label":406,"values":407},"health","Salud",[399,399],{"id":409,"label":410,"values":411},"enemy","Enemigo",[399,399],"Conocimiento vs estado actual",[414,417],{"id":415,"label":416},"knowledge","RAG \u002F conocimiento",{"id":418,"label":419},"state","Estado actual","comparison",{},{"id":423,"data":424,"type":226,"tunes":428},"dont-mix",{"body":425,"title":426,"variant":427},"RAG responde: \u003Cstrong>¿Qué es generalmente verdad?\u003C\u002Fstrong>\u003Cbr>El estado responde: \u003Cstrong>¿Qué es verdad en este momento?\u003C\u002Fstrong>","No mezcles estos dos","warning",{},{"id":430,"data":431,"type":42,"tunes":433},"h-state-db",{"text":432,"level":238},"¿Qué es una base de datos de estado?",{},{"id":435,"data":436,"type":218,"tunes":438},"p-statedb-1",{"text":437},"Una base de datos de estado o almacén de estado es simplemente un lugar donde la aplicación guarda hechos actuales.",{},{"id":440,"data":441,"type":218,"tunes":443},"p-statedb-2",{"text":442},"En un juego, el motor ya sabe cosas como tu salud, posición, inventario, munición, misión actual, objetos cercanos y estado de los enemigos. Un sistema de IA puede exponer partes seleccionadas de ese estado al modelo.",{},{"id":445,"data":446,"type":218,"tunes":448},"p-statedb-3",{"text":447},"En una aplicación empresarial, la misma idea podría ser una base de datos de pedidos, un registro de cliente, un estado de proyecto o el valor actual de un sensor.",{},{"id":450,"data":451,"type":218,"tunes":453},"p-statedb-4",{"text":452},"El estado lo crea la propia aplicación a medida que ocurren las cosas. Si pierdes salud, el juego actualiza el valor de salud. Si recoges munición, el inventario cambia. Si se paga un pedido, el sistema empresarial cambia el estado del pedido.",{},{"id":455,"data":456,"type":226,"tunes":458},"state-rule",{"body":457,"title":265,"variant":225},"La aplicación crea y actualiza el \u003Cstrong>estado\u003C\u002Fstrong>. RAG busca \u003Cstrong>conocimiento\u003C\u002Fstrong>. El LLM usa ambos para decidir qué decir o hacer.",{},{"id":460,"data":461,"type":42,"tunes":463},"h-together",{"text":462,"level":238},"Cómo funcionan juntas las tres piezas",{},{"id":465,"data":466,"type":318,"tunes":484},"together-flow",{"steps":467,"title":483,"orientation":317},[468,471,474,477,480],{"label":469,"description":470},"1. Estado actual","La aplicación le dice a la IA qué es verdad ahora: salud 41%, AKM equipada, 23 balas.",{"label":472,"description":473},"2. RAG","El sistema recupera conocimiento útil: cómo funciona el arma, qué objeto de curación está disponible o una regla relevante.",{"label":475,"description":476},"3. LLM","El modelo recibe la pregunta, el estado actual y el conocimiento recuperado.",{"label":478,"description":479},"4. Razonamiento","El LLM combina esas entradas y decide qué respuesta o acción de alto nivel tiene sentido.",{"label":481,"description":482},"5. Aplicación","Si se requiere una acción, la aplicación o el motor del juego la ejecuta y actualiza el estado de nuevo.","LLM + estado + RAG",{},{"id":486,"data":487,"type":218,"tunes":489},"p-arch-intro",{"text":488},"Entonces la arquitectura básica es:",{},{"id":491,"data":492,"type":226,"tunes":495},"simple-architecture",{"body":493,"title":494,"variant":266},"\u003Cstrong>Estado = lo que es verdad ahora\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>RAG = conocimiento útil\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>LLM = entiende, razona y escribe\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Aplicación = realiza la acción real\u003C\u002Fstrong>","La arquitectura más simple",{},{"id":497,"data":498,"type":42,"tunes":500},"h-vector",{"text":499,"level":238},"¿RAG siempre usa una base de datos vectorial?",{},{"id":502,"data":503,"type":218,"tunes":505},"p-vector-1",{"text":504},"No.",{},{"id":507,"data":508,"type":218,"tunes":510},"p-vector-2",{"text":509},"Una base de datos vectorial es una forma común de construir búsqueda semántica, pero no es la definición de RAG.",{},{"id":512,"data":513,"type":218,"tunes":515},"p-vector-3",{"text":514},"La parte importante es la recuperación: el sistema encuentra información externa relevante y la añade al contexto del LLM antes de que se genere la respuesta.",{},{"id":517,"data":518,"type":218,"tunes":520},"p-vector-4",{"text":519},"La Búsqueda de Archivos de OpenAI, por ejemplo, puede trabajar con archivos almacenados en almacenes vectoriales. Los archivos se dividen en fragmentos más pequeños para que el sistema pueda recuperar las partes relevantes para una pregunta. Esa es una implementación de la misma idea básica.",{},{"id":522,"data":523,"type":42,"tunes":525},"h-embedding",{"text":524,"level":238},"¿Qué es un embedding, en lenguaje sencillo?",{},{"id":527,"data":528,"type":218,"tunes":530},"p-emb-1",{"text":529},"No necesitas entender los embeddings para entender RAG.",{},{"id":532,"data":533,"type":218,"tunes":535},"p-emb-2",{"text":534},"Pero la versión simple es esta: un embedding es una representación numérica del significado. Ayuda a un sistema de búsqueda a encontrar texto que es conceptualmente similar incluso cuando las palabras no son exactamente las mismas.",{},{"id":537,"data":538,"type":218,"tunes":540},"p-emb-3",{"text":539},"Por ejemplo, una búsqueda normal por palabras clave puede buscar las palabras exactas \"reparación de coche\". La búsqueda semántica también puede entender que \"arreglar mi vehículo\" trata sobre un tema similar.",{},{"id":542,"data":543,"type":218,"tunes":545},"p-emb-4",{"text":544},"Eso hace que los embeddings sean útiles para RAG, pero RAG también puede usar búsqueda por palabras clave, consultas a bases de datos o una combinación de varios métodos.",{},{"id":547,"data":548,"type":42,"tunes":550},"h-memory",{"text":549,"level":238},"RAG tampoco es memoria",{},{"id":552,"data":553,"type":218,"tunes":555},"p-memory-1",{"text":554},"La memoria es otro concepto que a menudo se mezcla con RAG.",{},{"id":557,"data":558,"type":218,"tunes":560},"p-memory-2",{"text":559},"La memoria suele ser información que el sistema guarda sobre interacciones previas o eventos previos. RAG es el mecanismo utilizado para recuperar conocimiento relevante cuando se necesita.",{},{"id":562,"data":563,"type":359,"tunes":589},"parts-table",{"content":564,"stretched":43,"withHeadings":14},[565,568,571,574,577,580,583,586],[566,567],"Parte","Significado simple",[569,570],"LLM","La parte que entiende y genera lenguaje",[572,573],"RAG","La parte que busca conocimiento relevante antes de la respuesta",[575,576],"Base de conocimiento","La información que RAG puede buscar",[578,579],"Estado","Lo que es verdad ahora mismo en la aplicación o el mundo",[581,582],"Memoria","Información conservada de interacciones o eventos previos",[584,585],"Herramienta \u002F acción","Algo que la IA tiene permitido llamar o pedir a la aplicación que haga",[587,588],"Contexto","La información colocada actualmente frente al LLM para esta solicitud",{},{"id":591,"data":592,"type":42,"tunes":594},"h-pubg",{"text":593,"level":238},"Un ejemplo real de juego: PUBG Ally",{},{"id":596,"data":597,"type":218,"tunes":599},"p-pubg-1",{"text":598},"PUBG Ally es un ejemplo útil porque hace visible la diferencia.",{},{"id":601,"data":602,"type":218,"tunes":604},"p-pubg-2",{"text":603},"KRAFTON describe el estado de la partida en vivo como una fuente de verdad separada. El juego expone los hechos actuales a través de herramientas de observación: arma actual, munición, salud, estado de la zona segura, objetos cercanos y situación de combate.",{},{"id":606,"data":607,"type":218,"tunes":609},"p-pubg-3",{"text":608},"La consulta de conocimiento es una tarea diferente. El sistema puede usar conocimiento curado sobre armas, accesorios, objetos y reglas. El SDK ACE Game Agent de NVIDIA también expone una API RAG separada para recuperar conocimiento de bases de datos creadas por desarrolladores.",{},{"id":611,"data":612,"type":218,"tunes":614},"p-pubg-4",{"text":613},"Eso nos da la separación clara: el motor del juego dice qué está pasando ahora, la recuperación proporciona conocimiento relevante y el modelo de lenguaje decide qué significa la información.",{},{"id":616,"data":617,"type":622,"tunes":623},"ref-pubg",{"url":618,"title":619,"excerpt":620,"ctaLabel":621},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","PUBG Ally muestra por qué los compañeros de equipo de IA necesitan dos cerebros: reflejos rápidos y razonamiento lento","Un ejemplo práctico de juego que muestra cómo el estado en vivo, el razonamiento del lenguaje y el control determinista del lado del juego pueden funcionar juntos.","Leer el artículo sobre la arquitectura de PUBG Ally","referralArticle",{},{"id":625,"data":626,"type":42,"tunes":628},"h-complete",{"text":627,"level":238},"Un ejemplo completo",{},{"id":630,"data":631,"type":218,"tunes":633},"p-complete-1",{"text":632},"Imagina que le dices a un compañero de equipo de IA: “Tengo poca salud. ¿Deberíamos atacar?”",{},{"id":635,"data":636,"type":318,"tunes":654},"complete-flow",{"steps":637,"title":653,"orientation":317},[638,640,642,644,647,650],{"label":578,"description":639},"El juego informa: salud 24%, un enemigo cerca, dos objetos de curación disponibles.",{"label":572,"description":641},"El sistema de conocimiento recupera las reglas relevantes para el objeto de curación y quizás información sobre el arma actual o la mecánica táctica.",{"label":569,"description":643},"El modelo combina tu solicitud, el estado actual y el conocimiento recuperado.",{"label":645,"description":646},"Decisión","Concluye que curarse primero es más seguro que atacar de inmediato.",{"label":648,"description":649},"Herramienta \u002F motor del juego","El agente solicita una acción de juego legal, como moverse a cubierto o usar el objeto de curación.",{"label":651,"description":652},"Nuevo estado","El juego ejecuta la acción e informa la situación actualizada de vuelta al agente.","Qué sucede después",{},{"id":656,"data":657,"type":218,"tunes":659},"p-complete-2",{"text":658},"RAG no controló al personaje. La base de datos de estado no razonó. El LLM no cambió directamente el juego. Cada parte tenía una tarea.",{},{"id":661,"data":662,"type":42,"tunes":664},"h-why",{"text":663,"level":238},"¿Por qué usar RAG en absoluto?",{},{"id":666,"data":667,"type":218,"tunes":669},"p-why-1",{"text":668},"Porque poner cada documento, regla y registro de base de datos en cada prompt sería lento, costoso y a menudo confuso.",{},{"id":671,"data":672,"type":218,"tunes":674},"p-why-2",{"text":673},"RAG permite al sistema seleccionar solo la información que es útil para la pregunta actual.",{},{"id":676,"data":677,"type":218,"tunes":679},"p-why-3",{"text":678},"También te permite actualizar la base de conocimiento sin reentrenar todo el modelo de lenguaje. Cambia el documento o la base de datos, reconstruye o actualiza el índice cuando sea necesario, y la siguiente recuperación podrá usar la información más reciente.",{},{"id":681,"data":682,"type":42,"tunes":684},"h-not-guarantee",{"text":683,"level":238},"Lo que RAG no garantiza",{},{"id":686,"data":687,"type":218,"tunes":689},"p-not-1",{"text":688},"RAG puede mejorar la fundamentación, pero no hace que una respuesta sea automáticamente correcta.",{},{"id":691,"data":692,"type":218,"tunes":694},"p-not-2",{"text":693},"El paso de recuperación puede encontrar el documento equivocado. El documento correcto puede estar desactualizado. El LLM puede malinterpretar una buena evidencia. O el estado actual puede haber cambiado.",{},{"id":696,"data":697,"type":218,"tunes":699},"p-not-3",{"text":698},"Por lo tanto, un sistema confiable tiene que validar por separado la recuperación, la frescura del estado y el razonamiento final del modelo.",{},{"id":701,"data":702,"type":42,"tunes":704},"h-mental",{"text":703,"level":238},"El modelo mental más fácil de recordar",{},{"id":706,"data":707,"type":420,"tunes":740},"mental-table",{"rows":708,"title":733,"layout":359,"columns":734},[709,713,717,721,725,729],{"id":710,"label":711,"values":712},"brain","Persona pensando",[399,399],{"id":714,"label":715,"values":716},"library","Buscar un libro de referencia",[399,399],{"id":718,"label":719,"values":720},"books","Libros en el estante",[399,399],{"id":722,"label":723,"values":724},"dashboard","Panel de control o panel de instrumentos actual",[399,399],{"id":726,"label":727,"values":728},"notes","Notas de reuniones anteriores",[399,399],{"id":730,"label":731,"values":732},"hands","Hacer algo en el mundo real",[399,399],"Piensa en un sistema de IA como una persona en un escritorio",[735,737],{"id":229,"label":736},"Analogía",{"id":738,"label":739},"system","Sistema de IA",{},{"id":742,"data":743,"type":226,"tunes":746},"remember",{"body":744,"title":745,"variant":293},"\u003Cstrong>LLM = cerebro.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>RAG = bibliotecario.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Base de conocimiento = biblioteca.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Estado = lo que dice el panel de control en este momento.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Herramientas = las manos que realmente pueden hacer algo.\u003C\u002Fstrong>","Si solo recuerdas esto",{},{"id":748,"data":749,"type":42,"tunes":751},"h-conclusion",{"text":750,"level":238},"Conclusión",{},{"id":753,"data":754,"type":218,"tunes":756},"p-conc-1",{"text":755},"RAG es mucho menos misterioso una vez que se separan las partes.",{},{"id":758,"data":759,"type":218,"tunes":761},"p-conc-2",{"text":760},"El LLM entiende y genera lenguaje. La aplicación mantiene el estado actual. La base de conocimiento almacena información. RAG encuentra la parte útil de esa información y la coloca en el contexto del LLM. Las herramientas o la aplicación realizan acciones reales.",{},{"id":763,"data":764,"type":218,"tunes":766},"p-conc-3",{"text":765},"Esa es la arquitectura básica detrás de muchos asistentes y agentes de IA modernos.",{},{"id":768,"data":769,"type":42,"tunes":771},"h-faq",{"text":770,"level":238},"Preguntas frecuentes",{},{"id":773,"data":774,"type":773,"tunes":801},"faq",{"items":775,"title":800},[776,780,784,788,792,796],{"id":777,"answer":778,"question":779},"faq1","RAG es un paso en el que una IA busca información relevante en una fuente de conocimiento antes de que el modelo de lenguaje escriba su respuesta.","¿Qué es RAG en términos simples?",{"id":781,"answer":782,"question":783},"faq2","No. La base de conocimiento puede estar completamente local en tu computadora o servidor.","¿RAG necesita Internet?",{"id":785,"answer":786,"question":787},"faq3","No. La base de datos o los archivos contienen la información. RAG es el proceso de recuperación que encuentra la parte útil y se la da al LLM.","¿RAG es lo mismo que una base de datos?",{"id":789,"answer":790,"question":791},"faq4","No. La memoria generalmente almacena interacciones o eventos previos. RAG recupera conocimiento relevante cuando se necesita.","¿RAG es lo mismo que memoria?",{"id":793,"answer":794,"question":795},"faq5","No necesariamente. El estado actual generalmente se obtiene directamente de la aplicación o de un almacén de estado. RAG se entiende mejor como la recuperación desde una fuente de conocimiento.","¿El estado actual de la aplicación es parte de RAG?",{"id":797,"answer":798,"question":799},"faq6","No. Puede proporcionar mejores evidencias, pero la recuperación aún puede ser incorrecta o estar desactualizada y el LLM aún puede razonar incorrectamente.","¿RAG hace que las respuestas de la IA sean correctas?","RAG en lenguaje sencillo",{},{"id":803,"data":804,"type":42,"tunes":806},"h-glossary",{"text":805,"level":238},"Glosario",{},{"id":808,"data":809,"type":808,"tunes":830},"glossary",{"title":810,"entries":811},"Los términos básicos",[812,815,818,821,823,826],{"term":569,"anchor":813,"definition":814},"llm","Un modelo de lenguaje que entiende y genera texto y puede razonar sobre la información colocada en su contexto.",{"term":572,"anchor":816,"definition":817},"rag","Generación Aumentada por Recuperación: recuperar información externa relevante y agregarla al contexto del modelo antes de generar una respuesta.",{"term":575,"anchor":819,"definition":820},"knowledge-base","Los archivos, documentos, registros u otra información que la recuperación puede buscar.",{"term":578,"anchor":418,"definition":822},"Los hechos actuales de una aplicación, sistema o mundo en un momento particular.",{"term":587,"anchor":824,"definition":825},"context","La información proporcionada actualmente al modelo de lenguaje para una solicitud o paso de razonamiento.",{"term":827,"anchor":828,"definition":829},"Embedding","embedding","Una representación numérica del significado que puede ayudar a la búsqueda semántica a encontrar información conceptualmente similar.",{},{"id":832,"data":833,"type":42,"tunes":835},"h-sources",{"text":834,"level":238},"Fuentes primarias",{},{"id":837,"data":838,"type":844,"tunes":845},"src-openai-vector",{"link":839,"meta":840},"https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fapi-reference\u002Fvector-stores-files",{"image":841,"title":842,"description":843},{"url":399},"OpenAI — Archivos de Vector Store","Documentación oficial que muestra cómo se pueden adjuntar archivos a los almacenes vectoriales, dividirlos en fragmentos y ponerlos a disposición para la recuperación de búsqueda de archivos.","linkTool",{},{"id":847,"data":848,"type":844,"tunes":854},"src-openai-quickstart",{"link":849,"meta":850},"https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fquickstart",{"image":851,"title":852,"description":853},{"url":399},"OpenAI — Guía de inicio rápido para desarrolladores","Documentación oficial de OpenAI que describe herramientas como la búsqueda de archivos para dar a los modelos acceso a información externa.",{},{"id":856,"data":857,"type":844,"tunes":863},"src-nvidia-ace",{"link":858,"meta":859},"https:\u002F\u002Fdeveloper.nvidia.com\u002Face-for-games",{"image":860,"title":861,"description":862},{"url":399},"NVIDIA Developer — ACE para juegos","Documentación oficial de NVIDIA que describe API separadas de Agente, Chat y RAG para conectar personajes de juegos con el estado del juego, conocimiento contextual y acciones impulsadas por modelos.",{},{"id":865,"data":866,"type":844,"tunes":872},"src-nvidia-pubg",{"link":867,"meta":868},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fhow-krafton-built-pubg-ally-a-co-playable-character-powered-by-nvidia-ace\u002F",{"image":869,"title":870,"description":871},{"url":399},"NVIDIA Developer — Cómo KRAFTON construyó PUBG Ally","Explicación técnica oficial que separa el estado de la partida en vivo de la búsqueda de conocimiento y el razonamiento del modelo de lenguaje.",{},"2.31","RAG suena complicado, pero la idea es simple: antes de que una IA responda, primero busca información útil de una fuente de conocimiento y le da esa información al modelo de lenguaje. Esta guía explica RAG, los LLM, el estado, la memoria y las herramientas usando un modelo mental simple.","\u002Fuploads\u002F2026\u002F09\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works-1790377492124-khjagt.webp","what-is-rag-the-simplest-explanation-of-how-it-works-1790377492124-khjagt","PUBLISHED","2026-09-25T19:03:00.000Z","2026-09-25T23:03:13.651Z","2026-09-25T23:41:17.272Z",{"en":882,"de":883,"sr":884,"es":885,"fr":886,"it":887,"ru":888,"zh":889},"\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works","\u002Fde\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works","\u002Fsr\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works","\u002Fes\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works","\u002Ffr\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works","\u002Fit\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works","\u002Fru\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works","\u002Fzh\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works",[891,895,899,903,907,911],{"id":892,"name":893,"slug":894},46,"Resumen","overview",{"id":896,"name":897,"slug":898},57,"Límites de datos","data-boundaries",{"id":900,"name":901,"slug":902},51,"Antipatrones","anti-patterns",{"id":904,"name":905,"slug":906},58,"Evaluación y compuertas de calidad","evaluation",{"id":908,"name":909,"slug":910},56,"Portafolio de casos de uso","use-case-portfolio",{"id":912,"name":913,"slug":914},60,"Controles de coste y latencia","cost-and-latency",{"id":916,"login":917,"email":918,"displayName":919},"20","rooth8233","aleksandar@stajic.de","Aleksandar Stajić",[921,1451],{"lang":922,"title":923,"content":924,"contentJson":925,"excerpt":1450},"en","What Is RAG? The Simplest Explanation of How It Works","{\"time\":1790377494031,\"blocks\":[{\"id\":\"intro\",\"type\":\"paragraph\",\"data\":{\"text\":\"RAG sounds complicated because the name is complicated. The idea is not. RAG simply means: before the AI answers, it first looks up relevant information from a knowledge source and gives that information to the language model.\"},\"tunes\":{}},{\"id\":\"one-sentence\",\"type\":\"callout\",\"data\":{\"variant\":\"info\",\"title\":\"RAG in one sentence\",\"body\":\"\u003Cstrong>RAG is the step where an AI searches a knowledge base for useful information before the LLM writes the answer.\u003C\u002Fstrong>\"},\"tunes\":{}},{\"id\":\"analogy\",\"type\":\"paragraph\",\"data\":{\"text\":\"Think of an LLM as a smart person sitting at a desk. RAG is the librarian who brings the right page from the right book. The LLM then reads that page and answers you.\"},\"tunes\":{}},{\"id\":\"toc\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"h-llm\",\"type\":\"header\",\"data\":{\"text\":\"First: what does the LLM do?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-llm-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The LLM is the part that understands language and produces language. It can read your question, understand instructions, compare information, explain something and write an answer.\"},\"tunes\":{}},{\"id\":\"p-llm-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"But the LLM does not automatically know what is currently inside your company database, your game session, your private documents or a file you created five minutes ago.\"},\"tunes\":{}},{\"id\":\"p-llm-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"It only knows what is already inside the model plus whatever information the application gives it in the current request.\"},\"tunes\":{}},{\"id\":\"llm-rule\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"Simple rule\",\"body\":\"The LLM \u003Cstrong>thinks and writes\u003C\u002Fstrong>. It does not automatically own all of your current data.\"},\"tunes\":{}},{\"id\":\"h-kb\",\"type\":\"header\",\"data\":{\"text\":\"Then: what is the knowledge base?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-kb-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A knowledge base is simply information the application can search.\"},\"tunes\":{}},{\"id\":\"p-kb-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"It could contain PDFs, manuals, product documentation, support articles, contracts, game rules, weapon data, internal company documents, database records or other text.\"},\"tunes\":{}},{\"id\":\"p-kb-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The knowledge base can be local on your own machine. It can be on a server. It can be in a vector database. It can also be built from normal files. RAG does not mean Internet.\"},\"tunes\":{}},{\"id\":\"no-internet\",\"type\":\"callout\",\"data\":{\"variant\":\"success\",\"title\":\"Important\",\"body\":\"\u003Cstrong>RAG does not require the Internet.\u003C\u002Fstrong> The information can be completely local.\"},\"tunes\":{}},{\"id\":\"h-rag\",\"type\":\"header\",\"data\":{\"text\":\"So what does RAG actually do?\",\"level\":2},\"tunes\":{}},{\"id\":\"rag-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"The whole RAG process\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. You ask a question\",\"description\":\"For example: Which ammunition does this weapon use?\"},{\"label\":\"2. RAG searches the knowledge base\",\"description\":\"The system looks for the small pieces of information most relevant to your question.\"},{\"label\":\"3. RAG gives those pieces to the LLM\",\"description\":\"The LLM receives the question plus the retrieved information.\"},{\"label\":\"4. The LLM writes the answer\",\"description\":\"It uses the retrieved information as context for the response.\"}]},\"tunes\":{}},{\"id\":\"rag-that-is-it\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is RAG.\"},\"tunes\":{}},{\"id\":\"rag-name\",\"type\":\"paragraph\",\"data\":{\"text\":\"The full name is Retrieval-Augmented Generation. Retrieval means finding the relevant information. Augmented means adding that information to the model's context. Generation means the LLM writes the final answer.\"},\"tunes\":{}},{\"id\":\"h-example\",\"type\":\"header\",\"data\":{\"text\":\"A very simple example\",\"level\":2},\"tunes\":{}},{\"id\":\"p-ex-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Imagine you have a local knowledge base about a game.\"},\"tunes\":{}},{\"id\":\"kb-table\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Knowledge base contains\",\"Example\"],[\"Weapons\",\"AKM uses 7.62 mm ammunition\"],[\"Healing items\",\"Med Kit restores health\"],[\"Attachments\",\"This attachment works with these weapons\"],[\"Map rules\",\"This zone behaves in this way\"]]},\"tunes\":{}},{\"id\":\"p-ex-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"You ask: “Which ammunition does the AKM use?”\"},\"tunes\":{}},{\"id\":\"p-ex-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"RAG searches the knowledge base and finds the entry about the AKM. It gives that small piece of information to the LLM. The LLM then answers: “The AKM uses 7.62 mm ammunition.”\"},\"tunes\":{}},{\"id\":\"p-ex-4\",\"type\":\"paragraph\",\"data\":{\"text\":\"The LLM did not need the entire database. RAG only brought the useful part.\"},\"tunes\":{}},{\"id\":\"h-state\",\"type\":\"header\",\"data\":{\"text\":\"Now the important part: RAG is not the current state\",\"level\":2},\"tunes\":{}},{\"id\":\"p-state-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is where many explanations become confusing.\"},\"tunes\":{}},{\"id\":\"p-state-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"RAG usually gives the AI knowledge. A state system gives the AI facts about what is true right now.\"},\"tunes\":{}},{\"id\":\"knowledge-state\",\"type\":\"comparison\",\"data\":{\"title\":\"Knowledge vs current state\",\"layout\":\"table\",\"columns\":[{\"id\":\"knowledge\",\"label\":\"RAG \u002F knowledge\"},{\"id\":\"state\",\"label\":\"Current state\"}],\"rows\":[{\"id\":\"weapon\",\"label\":\"Weapon\",\"values\":[\"\",\"\"]},{\"id\":\"ammo\",\"label\":\"Ammunition\",\"values\":[\"\",\"\"]},{\"id\":\"health\",\"label\":\"Health\",\"values\":[\"\",\"\"]},{\"id\":\"enemy\",\"label\":\"Enemy\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"dont-mix\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"Do not mix these two\",\"body\":\"RAG answers: \u003Cstrong>What is generally true?\u003C\u002Fstrong>\u003Cbr>State answers: \u003Cstrong>What is true right now?\u003C\u002Fstrong>\"},\"tunes\":{}},{\"id\":\"h-state-db\",\"type\":\"header\",\"data\":{\"text\":\"What is a state database?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-statedb-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A state database or state store is simply a place where the application keeps current facts.\"},\"tunes\":{}},{\"id\":\"p-statedb-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"In a game, the engine already knows things such as your health, position, inventory, ammunition, current mission, nearby objects and enemy status. An AI system can expose selected parts of that state to the model.\"},\"tunes\":{}},{\"id\":\"p-statedb-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"In a business application, the same idea could be an order database, a customer record, a project status or the current value of a sensor.\"},\"tunes\":{}},{\"id\":\"p-statedb-4\",\"type\":\"paragraph\",\"data\":{\"text\":\"The state is created by the application itself as things happen. If you lose health, the game updates the health value. If you pick up ammunition, the inventory changes. If an order is paid, the business system changes the order status.\"},\"tunes\":{}},{\"id\":\"state-rule\",\"type\":\"callout\",\"data\":{\"variant\":\"info\",\"title\":\"Simple rule\",\"body\":\"The application creates and updates \u003Cstrong>state\u003C\u002Fstrong>. RAG searches \u003Cstrong>knowledge\u003C\u002Fstrong>. The LLM uses both to decide what to say or do.\"},\"tunes\":{}},{\"id\":\"h-together\",\"type\":\"header\",\"data\":{\"text\":\"How the three pieces work together\",\"level\":2},\"tunes\":{}},{\"id\":\"together-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"LLM + state + RAG\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Current state\",\"description\":\"The application tells the AI what is true now: health 41%, AKM equipped, 23 rounds.\"},{\"label\":\"2. RAG\",\"description\":\"The system retrieves useful knowledge: how the weapon works, which healing item is available, or a relevant rule.\"},{\"label\":\"3. LLM\",\"description\":\"The model receives the question, current state and retrieved knowledge.\"},{\"label\":\"4. Reasoning\",\"description\":\"The LLM combines those inputs and decides what answer or high-level action makes sense.\"},{\"label\":\"5. Application\",\"description\":\"If an action is required, the application or game engine executes it and updates the state again.\"}]},\"tunes\":{}},{\"id\":\"p-arch-intro\",\"type\":\"paragraph\",\"data\":{\"text\":\"So the basic architecture is:\"},\"tunes\":{}},{\"id\":\"simple-architecture\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"The simplest architecture\",\"body\":\"\u003Cstrong>State = what is true now\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>RAG = useful knowledge\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>LLM = understands, reasons and writes\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Application = performs the real action\u003C\u002Fstrong>\"},\"tunes\":{}},{\"id\":\"h-vector\",\"type\":\"header\",\"data\":{\"text\":\"Does RAG always use a vector database?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-vector-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"No.\"},\"tunes\":{}},{\"id\":\"p-vector-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"A vector database is a common way to build semantic search, but it is not the definition of RAG.\"},\"tunes\":{}},{\"id\":\"p-vector-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The important part is retrieval: the system finds relevant external information and adds it to the LLM's context before the answer is generated.\"},\"tunes\":{}},{\"id\":\"p-vector-4\",\"type\":\"paragraph\",\"data\":{\"text\":\"OpenAI's File Search, for example, can work with files stored in vector stores. Files are chunked into smaller pieces so the system can retrieve the parts that are relevant to a question. That is one implementation of the same basic idea.\"},\"tunes\":{}},{\"id\":\"h-embedding\",\"type\":\"header\",\"data\":{\"text\":\"What is an embedding, in plain English?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-emb-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"You do not need to understand embeddings to understand RAG.\"},\"tunes\":{}},{\"id\":\"p-emb-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"But the simple version is this: an embedding is a numerical representation of meaning. It helps a search system find text that is conceptually similar even when the words are not exactly the same.\"},\"tunes\":{}},{\"id\":\"p-emb-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"For example, a normal keyword search may look for the exact words “car repair.” Semantic search can also understand that “fix my vehicle” is about a similar topic.\"},\"tunes\":{}},{\"id\":\"p-emb-4\",\"type\":\"paragraph\",\"data\":{\"text\":\"That makes embeddings useful for RAG, but RAG can also use keyword search, database queries or a hybrid of several methods.\"},\"tunes\":{}},{\"id\":\"h-memory\",\"type\":\"header\",\"data\":{\"text\":\"RAG is not memory either\",\"level\":2},\"tunes\":{}},{\"id\":\"p-memory-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Memory is another concept that is often mixed together with RAG.\"},\"tunes\":{}},{\"id\":\"p-memory-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Memory is usually information the system keeps about previous interactions or previous events. RAG is the mechanism used to retrieve relevant knowledge when it is needed.\"},\"tunes\":{}},{\"id\":\"parts-table\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Part\",\"Simple meaning\"],[\"LLM\",\"The part that understands and generates language\"],[\"RAG\",\"The part that looks up relevant knowledge before the answer\"],[\"Knowledge base\",\"The information RAG can search\"],[\"State\",\"What is true right now in the application or world\"],[\"Memory\",\"Information kept from previous interactions or events\"],[\"Tool \u002F action\",\"Something the AI is allowed to call or ask the application to do\"],[\"Context\",\"The information currently placed in front of the LLM for this request\"]]},\"tunes\":{}},{\"id\":\"h-pubg\",\"type\":\"header\",\"data\":{\"text\":\"A real game example: PUBG Ally\",\"level\":2},\"tunes\":{}},{\"id\":\"p-pubg-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"PUBG Ally is a useful example because it makes the difference visible.\"},\"tunes\":{}},{\"id\":\"p-pubg-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"KRAFTON describes live match state as a separate source of truth. The game exposes current facts through observation tools: current weapon, ammunition, health, safe-zone status, nearby items and combat situation.\"},\"tunes\":{}},{\"id\":\"p-pubg-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Knowledge lookup is a different job. The system can use curated knowledge about weapons, attachments, items and rules. NVIDIA's ACE Game Agent SDK also exposes a separate RAG API for retrieving knowledge from developer-built databases.\"},\"tunes\":{}},{\"id\":\"p-pubg-4\",\"type\":\"paragraph\",\"data\":{\"text\":\"That gives us the clean separation: the game engine says what is happening now, retrieval provides relevant knowledge, and the language model decides what the information means.\"},\"tunes\":{}},{\"id\":\"ref-pubg\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning\",\"title\":\"PUBG Ally Shows Why AI Teammates Need Two Brains: Fast Reflexes and Slow Reasoning\",\"excerpt\":\"A practical game example showing how live state, language reasoning and deterministic game-side control can work together.\",\"ctaLabel\":\"Read the PUBG Ally architecture article\"},\"tunes\":{}},{\"id\":\"h-complete\",\"type\":\"header\",\"data\":{\"text\":\"One complete example\",\"level\":2},\"tunes\":{}},{\"id\":\"p-complete-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Imagine you tell an AI teammate: “I am low on health. Should we attack?”\"},\"tunes\":{}},{\"id\":\"complete-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"What happens next\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"State\",\"description\":\"The game reports: health 24%, one enemy nearby, two healing items available.\"},{\"label\":\"RAG\",\"description\":\"The knowledge system retrieves the relevant rules for the healing item and perhaps information about the current weapon or tactical mechanic.\"},{\"label\":\"LLM\",\"description\":\"The model combines your request, the current state and the retrieved knowledge.\"},{\"label\":\"Decision\",\"description\":\"It concludes that healing first is safer than attacking immediately.\"},{\"label\":\"Tool \u002F game engine\",\"description\":\"The agent requests a legal game action such as moving to cover or using the healing item.\"},{\"label\":\"New state\",\"description\":\"The game executes the action and reports the updated situation back to the agent.\"}]},\"tunes\":{}},{\"id\":\"p-complete-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"RAG did not control the character. The state database did not reason. The LLM did not directly change the game. Each part had one job.\"},\"tunes\":{}},{\"id\":\"h-why\",\"type\":\"header\",\"data\":{\"text\":\"Why use RAG at all?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-why-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Because putting every document, rule and database record into every prompt would be slow, expensive and often confusing.\"},\"tunes\":{}},{\"id\":\"p-why-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"RAG lets the system select only the information that is useful for the current question.\"},\"tunes\":{}},{\"id\":\"p-why-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"It also lets you update the knowledge base without retraining the entire language model. Change the document or database, rebuild or refresh the index when necessary, and the next retrieval can use the newer information.\"},\"tunes\":{}},{\"id\":\"h-not-guarantee\",\"type\":\"header\",\"data\":{\"text\":\"What RAG does not guarantee\",\"level\":2},\"tunes\":{}},{\"id\":\"p-not-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"RAG can improve grounding, but it does not make an answer automatically correct.\"},\"tunes\":{}},{\"id\":\"p-not-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The retrieval step can find the wrong document. The correct document can be outdated. The LLM can misunderstand good evidence. Or the current state can have changed.\"},\"tunes\":{}},{\"id\":\"p-not-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"A reliable system therefore has to validate retrieval, state freshness and the model's final reasoning separately.\"},\"tunes\":{}},{\"id\":\"h-mental\",\"type\":\"header\",\"data\":{\"text\":\"The easiest mental model to remember\",\"level\":2},\"tunes\":{}},{\"id\":\"mental-table\",\"type\":\"comparison\",\"data\":{\"title\":\"Think of an AI system like a person at a desk\",\"layout\":\"table\",\"columns\":[{\"id\":\"analogy\",\"label\":\"Analogy\"},{\"id\":\"system\",\"label\":\"AI system\"}],\"rows\":[{\"id\":\"brain\",\"label\":\"Person thinking\",\"values\":[\"\",\"\"]},{\"id\":\"library\",\"label\":\"Finding a reference book\",\"values\":[\"\",\"\"]},{\"id\":\"books\",\"label\":\"Books on the shelf\",\"values\":[\"\",\"\"]},{\"id\":\"dashboard\",\"label\":\"Current dashboard or instrument panel\",\"values\":[\"\",\"\"]},{\"id\":\"notes\",\"label\":\"Notes from earlier meetings\",\"values\":[\"\",\"\"]},{\"id\":\"hands\",\"label\":\"Doing something in the real world\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"remember\",\"type\":\"callout\",\"data\":{\"variant\":\"success\",\"title\":\"If you remember only this\",\"body\":\"\u003Cstrong>LLM = brain.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>RAG = librarian.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Knowledge base = library.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>State = what the dashboard says right now.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Tools = the hands that can actually do something.\u003C\u002Fstrong>\"},\"tunes\":{}},{\"id\":\"h-conclusion\",\"type\":\"header\",\"data\":{\"text\":\"Conclusion\",\"level\":2},\"tunes\":{}},{\"id\":\"p-conc-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"RAG is much less mysterious once the parts are separated.\"},\"tunes\":{}},{\"id\":\"p-conc-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The LLM understands and generates language. The application maintains current state. The knowledge base stores information. RAG finds the useful part of that information and puts it into the LLM's context. Tools or the application perform real actions.\"},\"tunes\":{}},{\"id\":\"p-conc-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is the basic architecture behind many modern AI assistants and agents.\"},\"tunes\":{}},{\"id\":\"h-faq\",\"type\":\"header\",\"data\":{\"text\":\"FAQ\",\"level\":2},\"tunes\":{}},{\"id\":\"faq\",\"type\":\"faq\",\"data\":{\"title\":\"RAG in plain English\",\"items\":[{\"id\":\"faq1\",\"question\":\"What is RAG in simple terms?\",\"answer\":\"RAG is a step where an AI searches a knowledge source for relevant information before the language model writes its answer.\"},{\"id\":\"faq2\",\"question\":\"Does RAG need the Internet?\",\"answer\":\"No. The knowledge base can be completely local on your computer or server.\"},{\"id\":\"faq3\",\"question\":\"Is RAG the same as a database?\",\"answer\":\"No. The database or files contain the information. RAG is the retrieval process that finds the useful part and gives it to the LLM.\"},{\"id\":\"faq4\",\"question\":\"Is RAG the same as memory?\",\"answer\":\"No. Memory usually stores previous interactions or events. RAG retrieves relevant knowledge when it is needed.\"},{\"id\":\"faq5\",\"question\":\"Is current application state part of RAG?\",\"answer\":\"Not necessarily. Current state is usually obtained directly from the application or a state store. RAG is better understood as retrieval from a knowledge source.\"},{\"id\":\"faq6\",\"question\":\"Does RAG make AI answers correct?\",\"answer\":\"No. It can provide better evidence, but retrieval can still be wrong or outdated and the LLM can still reason incorrectly.\"}]},\"tunes\":{}},{\"id\":\"h-glossary\",\"type\":\"header\",\"data\":{\"text\":\"Glossary\",\"level\":2},\"tunes\":{}},{\"id\":\"glossary\",\"type\":\"glossary\",\"data\":{\"title\":\"The basic terms\",\"entries\":[{\"term\":\"LLM\",\"definition\":\"A language model that understands and generates text and can reason over information placed in its context.\",\"anchor\":\"llm\"},{\"term\":\"RAG\",\"definition\":\"Retrieval-Augmented Generation: retrieving relevant external information and adding it to the model's context before generating an answer.\",\"anchor\":\"rag\"},{\"term\":\"Knowledge base\",\"definition\":\"The files, documents, records or other information that retrieval can search.\",\"anchor\":\"knowledge-base\"},{\"term\":\"State\",\"definition\":\"The current facts of an application, system or world at a particular moment.\",\"anchor\":\"state\"},{\"term\":\"Context\",\"definition\":\"The information currently supplied to the language model for one request or reasoning step.\",\"anchor\":\"context\"},{\"term\":\"Embedding\",\"definition\":\"A numerical representation of meaning that can help semantic search find conceptually similar information.\",\"anchor\":\"embedding\"}]},\"tunes\":{}},{\"id\":\"h-sources\",\"type\":\"header\",\"data\":{\"text\":\"Primary sources\",\"level\":2},\"tunes\":{}},{\"id\":\"src-openai-vector\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fapi-reference\u002Fvector-stores-files\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"OpenAI — Vector Store Files\",\"description\":\"Official documentation showing how files can be attached to vector stores, chunked and made available to file-search retrieval.\"}},\"tunes\":{}},{\"id\":\"src-openai-quickstart\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fquickstart\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"OpenAI — Developer Quickstart\",\"description\":\"Official OpenAI documentation describing tools such as file search for giving models access to external information.\"}},\"tunes\":{}},{\"id\":\"src-nvidia-ace\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.nvidia.com\u002Face-for-games\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA Developer — ACE for Games\",\"description\":\"Official NVIDIA documentation describing separate Agent, Chat and RAG APIs for connecting game characters to game state, contextual knowledge and model-driven actions.\"}},\"tunes\":{}},{\"id\":\"src-nvidia-pubg\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fhow-krafton-built-pubg-ally-a-co-playable-character-powered-by-nvidia-ace\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA Developer — How KRAFTON Built PUBG Ally\",\"description\":\"Official technical explanation separating live match state from knowledge lookup and language-model reasoning.\"}},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":926,"blocks":927,"version":1449},1790377494031,[928,932,937,941,945,949,953,957,961,966,970,974,978,982,987,991,1008,1012,1016,1020,1024,1043,1047,1051,1055,1059,1063,1067,1089,1094,1098,1102,1106,1110,1114,1118,1122,1140,1144,1149,1153,1156,1160,1164,1168,1172,1176,1180,1184,1188,1192,1196,1200,1226,1230,1234,1238,1242,1246,1252,1256,1260,1280,1284,1288,1292,1296,1300,1304,1308,1312,1316,1320,1348,1353,1357,1361,1365,1369,1373,1396,1400,1417,1421,1428,1435,1442],{"id":215,"data":929,"type":218,"tunes":931},{"text":930},"RAG sounds complicated because the name is complicated. The idea is not. RAG simply means: before the AI answers, it first looks up relevant information from a knowledge source and gives that information to the language model.",{},{"id":221,"data":933,"type":226,"tunes":936},{"body":934,"title":935,"variant":225},"\u003Cstrong>RAG is the step where an AI searches a knowledge base for useful information before the LLM writes the answer.\u003C\u002Fstrong>","RAG in one sentence",{},{"id":229,"data":938,"type":218,"tunes":940},{"text":939},"Think of an LLM as a smart person sitting at a desk. RAG is the librarian who brings the right page from the right book. The LLM then reads that page and answers you.",{},{"id":234,"data":942,"type":239,"tunes":944},{"title":943,"maxLevel":237,"minLevel":238},"Contents",{},{"id":242,"data":946,"type":42,"tunes":948},{"text":947,"level":238},"First: what does the LLM do?",{},{"id":247,"data":950,"type":218,"tunes":952},{"text":951},"The LLM is the part that understands language and produces language. It can read your question, understand instructions, compare information, explain something and write an answer.",{},{"id":252,"data":954,"type":218,"tunes":956},{"text":955},"But the LLM does not automatically know what is currently inside your company database, your game session, your private documents or a file you created five minutes ago.",{},{"id":257,"data":958,"type":218,"tunes":960},{"text":959},"It only knows what is already inside the model plus whatever information the application gives it in the current request.",{},{"id":262,"data":962,"type":226,"tunes":965},{"body":963,"title":964,"variant":266},"The LLM \u003Cstrong>thinks and writes\u003C\u002Fstrong>. It does not automatically own all of your current data.","Simple rule",{},{"id":269,"data":967,"type":42,"tunes":969},{"text":968,"level":238},"Then: what is the knowledge base?",{},{"id":274,"data":971,"type":218,"tunes":973},{"text":972},"A knowledge base is simply information the application can search.",{},{"id":279,"data":975,"type":218,"tunes":977},{"text":976},"It could contain PDFs, manuals, product documentation, support articles, contracts, game rules, weapon data, internal company documents, database records or other text.",{},{"id":284,"data":979,"type":218,"tunes":981},{"text":980},"The knowledge base can be local on your own machine. It can be on a server. It can be in a vector database. It can also be built from normal files. RAG does not mean Internet.",{},{"id":289,"data":983,"type":226,"tunes":986},{"body":984,"title":985,"variant":293},"\u003Cstrong>RAG does not require the Internet.\u003C\u002Fstrong> The information can be completely local.","Important",{},{"id":296,"data":988,"type":42,"tunes":990},{"text":989,"level":238},"So what does RAG actually do?",{},{"id":301,"data":992,"type":318,"tunes":1007},{"steps":993,"title":1006,"orientation":317},[994,997,1000,1003],{"label":995,"description":996},"1. You ask a question","For example: Which ammunition does this weapon use?",{"label":998,"description":999},"2. RAG searches the knowledge base","The system looks for the small pieces of information most relevant to your question.",{"label":1001,"description":1002},"3. RAG gives those pieces to the LLM","The LLM receives the question plus the retrieved information.",{"label":1004,"description":1005},"4. The LLM writes the answer","It uses the retrieved information as context for the response.","The whole RAG process",{},{"id":321,"data":1009,"type":218,"tunes":1011},{"text":1010},"That is RAG.",{},{"id":326,"data":1013,"type":218,"tunes":1015},{"text":1014},"The full name is Retrieval-Augmented Generation. Retrieval means finding the relevant information. Augmented means adding that information to the model's context. Generation means the LLM writes the final answer.",{},{"id":331,"data":1017,"type":42,"tunes":1019},{"text":1018,"level":238},"A very simple example",{},{"id":336,"data":1021,"type":218,"tunes":1023},{"text":1022},"Imagine you have a local knowledge base about a game.",{},{"id":341,"data":1025,"type":359,"tunes":1042},{"content":1026,"stretched":43,"withHeadings":14},[1027,1030,1033,1036,1039],[1028,1029],"Knowledge base contains","Example",[1031,1032],"Weapons","AKM uses 7.62 mm ammunition",[1034,1035],"Healing items","Med Kit restores health",[1037,1038],"Attachments","This attachment works with these weapons",[1040,1041],"Map rules","This zone behaves in this way",{},{"id":362,"data":1044,"type":218,"tunes":1046},{"text":1045},"You ask: “Which ammunition does the AKM use?”",{},{"id":367,"data":1048,"type":218,"tunes":1050},{"text":1049},"RAG searches the knowledge base and finds the entry about the AKM. It gives that small piece of information to the LLM. The LLM then answers: “The AKM uses 7.62 mm ammunition.”",{},{"id":372,"data":1052,"type":218,"tunes":1054},{"text":1053},"The LLM did not need the entire database. RAG only brought the useful part.",{},{"id":377,"data":1056,"type":42,"tunes":1058},{"text":1057,"level":238},"Now the important part: RAG is not the current state",{},{"id":382,"data":1060,"type":218,"tunes":1062},{"text":1061},"This is where many explanations become confusing.",{},{"id":387,"data":1064,"type":218,"tunes":1066},{"text":1065},"RAG usually gives the AI knowledge. A state system gives the AI facts about what is true right now.",{},{"id":392,"data":1068,"type":420,"tunes":1088},{"rows":1069,"title":1082,"layout":359,"columns":1083},[1070,1073,1076,1079],{"id":396,"label":1071,"values":1072},"Weapon",[399,399],{"id":401,"label":1074,"values":1075},"Ammunition",[399,399],{"id":405,"label":1077,"values":1078},"Health",[399,399],{"id":409,"label":1080,"values":1081},"Enemy",[399,399],"Knowledge vs current state",[1084,1086],{"id":415,"label":1085},"RAG \u002F knowledge",{"id":418,"label":1087},"Current state",{},{"id":423,"data":1090,"type":226,"tunes":1093},{"body":1091,"title":1092,"variant":427},"RAG answers: \u003Cstrong>What is generally true?\u003C\u002Fstrong>\u003Cbr>State answers: \u003Cstrong>What is true right now?\u003C\u002Fstrong>","Do not mix these two",{},{"id":430,"data":1095,"type":42,"tunes":1097},{"text":1096,"level":238},"What is a state database?",{},{"id":435,"data":1099,"type":218,"tunes":1101},{"text":1100},"A state database or state store is simply a place where the application keeps current facts.",{},{"id":440,"data":1103,"type":218,"tunes":1105},{"text":1104},"In a game, the engine already knows things such as your health, position, inventory, ammunition, current mission, nearby objects and enemy status. An AI system can expose selected parts of that state to the model.",{},{"id":445,"data":1107,"type":218,"tunes":1109},{"text":1108},"In a business application, the same idea could be an order database, a customer record, a project status or the current value of a sensor.",{},{"id":450,"data":1111,"type":218,"tunes":1113},{"text":1112},"The state is created by the application itself as things happen. If you lose health, the game updates the health value. If you pick up ammunition, the inventory changes. If an order is paid, the business system changes the order status.",{},{"id":455,"data":1115,"type":226,"tunes":1117},{"body":1116,"title":964,"variant":225},"The application creates and updates \u003Cstrong>state\u003C\u002Fstrong>. RAG searches \u003Cstrong>knowledge\u003C\u002Fstrong>. The LLM uses both to decide what to say or do.",{},{"id":460,"data":1119,"type":42,"tunes":1121},{"text":1120,"level":238},"How the three pieces work together",{},{"id":465,"data":1123,"type":318,"tunes":1139},{"steps":1124,"title":1138,"orientation":317},[1125,1128,1130,1132,1135],{"label":1126,"description":1127},"1. Current state","The application tells the AI what is true now: health 41%, AKM equipped, 23 rounds.",{"label":472,"description":1129},"The system retrieves useful knowledge: how the weapon works, which healing item is available, or a relevant rule.",{"label":475,"description":1131},"The model receives the question, current state and retrieved knowledge.",{"label":1133,"description":1134},"4. Reasoning","The LLM combines those inputs and decides what answer or high-level action makes sense.",{"label":1136,"description":1137},"5. Application","If an action is required, the application or game engine executes it and updates the state again.","LLM + state + RAG",{},{"id":486,"data":1141,"type":218,"tunes":1143},{"text":1142},"So the basic architecture is:",{},{"id":491,"data":1145,"type":226,"tunes":1148},{"body":1146,"title":1147,"variant":266},"\u003Cstrong>State = what is true now\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>RAG = useful knowledge\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>LLM = understands, reasons and writes\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Application = performs the real action\u003C\u002Fstrong>","The simplest architecture",{},{"id":497,"data":1150,"type":42,"tunes":1152},{"text":1151,"level":238},"Does RAG always use a vector database?",{},{"id":502,"data":1154,"type":218,"tunes":1155},{"text":504},{},{"id":507,"data":1157,"type":218,"tunes":1159},{"text":1158},"A vector database is a common way to build semantic search, but it is not the definition of RAG.",{},{"id":512,"data":1161,"type":218,"tunes":1163},{"text":1162},"The important part is retrieval: the system finds relevant external information and adds it to the LLM's context before the answer is generated.",{},{"id":517,"data":1165,"type":218,"tunes":1167},{"text":1166},"OpenAI's File Search, for example, can work with files stored in vector stores. Files are chunked into smaller pieces so the system can retrieve the parts that are relevant to a question. That is one implementation of the same basic idea.",{},{"id":522,"data":1169,"type":42,"tunes":1171},{"text":1170,"level":238},"What is an embedding, in plain English?",{},{"id":527,"data":1173,"type":218,"tunes":1175},{"text":1174},"You do not need to understand embeddings to understand RAG.",{},{"id":532,"data":1177,"type":218,"tunes":1179},{"text":1178},"But the simple version is this: an embedding is a numerical representation of meaning. It helps a search system find text that is conceptually similar even when the words are not exactly the same.",{},{"id":537,"data":1181,"type":218,"tunes":1183},{"text":1182},"For example, a normal keyword search may look for the exact words “car repair.” Semantic search can also understand that “fix my vehicle” is about a similar topic.",{},{"id":542,"data":1185,"type":218,"tunes":1187},{"text":1186},"That makes embeddings useful for RAG, but RAG can also use keyword search, database queries or a hybrid of several methods.",{},{"id":547,"data":1189,"type":42,"tunes":1191},{"text":1190,"level":238},"RAG is not memory either",{},{"id":552,"data":1193,"type":218,"tunes":1195},{"text":1194},"Memory is another concept that is often mixed together with RAG.",{},{"id":557,"data":1197,"type":218,"tunes":1199},{"text":1198},"Memory is usually information the system keeps about previous interactions or previous events. RAG is the mechanism used to retrieve relevant knowledge when it is needed.",{},{"id":562,"data":1201,"type":359,"tunes":1225},{"content":1202,"stretched":43,"withHeadings":14},[1203,1206,1208,1210,1213,1216,1219,1222],[1204,1205],"Part","Simple meaning",[569,1207],"The part that understands and generates language",[572,1209],"The part that looks up relevant knowledge before the answer",[1211,1212],"Knowledge base","The information RAG can search",[1214,1215],"State","What is true right now in the application or world",[1217,1218],"Memory","Information kept from previous interactions or events",[1220,1221],"Tool \u002F action","Something the AI is allowed to call or ask the application to do",[1223,1224],"Context","The information currently placed in front of the LLM for this request",{},{"id":591,"data":1227,"type":42,"tunes":1229},{"text":1228,"level":238},"A real game example: PUBG Ally",{},{"id":596,"data":1231,"type":218,"tunes":1233},{"text":1232},"PUBG Ally is a useful example because it makes the difference visible.",{},{"id":601,"data":1235,"type":218,"tunes":1237},{"text":1236},"KRAFTON describes live match state as a separate source of truth. The game exposes current facts through observation tools: current weapon, ammunition, health, safe-zone status, nearby items and combat situation.",{},{"id":606,"data":1239,"type":218,"tunes":1241},{"text":1240},"Knowledge lookup is a different job. The system can use curated knowledge about weapons, attachments, items and rules. NVIDIA's ACE Game Agent SDK also exposes a separate RAG API for retrieving knowledge from developer-built databases.",{},{"id":611,"data":1243,"type":218,"tunes":1245},{"text":1244},"That gives us the clean separation: the game engine says what is happening now, retrieval provides relevant knowledge, and the language model decides what the information means.",{},{"id":616,"data":1247,"type":622,"tunes":1251},{"url":618,"title":1248,"excerpt":1249,"ctaLabel":1250},"PUBG Ally Shows Why AI Teammates Need Two Brains: Fast Reflexes and Slow Reasoning","A practical game example showing how live state, language reasoning and deterministic game-side control can work together.","Read the PUBG Ally architecture article",{},{"id":625,"data":1253,"type":42,"tunes":1255},{"text":1254,"level":238},"One complete example",{},{"id":630,"data":1257,"type":218,"tunes":1259},{"text":1258},"Imagine you tell an AI teammate: “I am low on health. Should we attack?”",{},{"id":635,"data":1261,"type":318,"tunes":1279},{"steps":1262,"title":1278,"orientation":317},[1263,1265,1267,1269,1272,1275],{"label":1214,"description":1264},"The game reports: health 24%, one enemy nearby, two healing items available.",{"label":572,"description":1266},"The knowledge system retrieves the relevant rules for the healing item and perhaps information about the current weapon or tactical mechanic.",{"label":569,"description":1268},"The model combines your request, the current state and the retrieved knowledge.",{"label":1270,"description":1271},"Decision","It concludes that healing first is safer than attacking immediately.",{"label":1273,"description":1274},"Tool \u002F game engine","The agent requests a legal game action such as moving to cover or using the healing item.",{"label":1276,"description":1277},"New state","The game executes the action and reports the updated situation back to the agent.","What happens next",{},{"id":656,"data":1281,"type":218,"tunes":1283},{"text":1282},"RAG did not control the character. The state database did not reason. The LLM did not directly change the game. Each part had one job.",{},{"id":661,"data":1285,"type":42,"tunes":1287},{"text":1286,"level":238},"Why use RAG at all?",{},{"id":666,"data":1289,"type":218,"tunes":1291},{"text":1290},"Because putting every document, rule and database record into every prompt would be slow, expensive and often confusing.",{},{"id":671,"data":1293,"type":218,"tunes":1295},{"text":1294},"RAG lets the system select only the information that is useful for the current question.",{},{"id":676,"data":1297,"type":218,"tunes":1299},{"text":1298},"It also lets you update the knowledge base without retraining the entire language model. Change the document or database, rebuild or refresh the index when necessary, and the next retrieval can use the newer information.",{},{"id":681,"data":1301,"type":42,"tunes":1303},{"text":1302,"level":238},"What RAG does not guarantee",{},{"id":686,"data":1305,"type":218,"tunes":1307},{"text":1306},"RAG can improve grounding, but it does not make an answer automatically correct.",{},{"id":691,"data":1309,"type":218,"tunes":1311},{"text":1310},"The retrieval step can find the wrong document. The correct document can be outdated. The LLM can misunderstand good evidence. Or the current state can have changed.",{},{"id":696,"data":1313,"type":218,"tunes":1315},{"text":1314},"A reliable system therefore has to validate retrieval, state freshness and the model's final reasoning separately.",{},{"id":701,"data":1317,"type":42,"tunes":1319},{"text":1318,"level":238},"The easiest mental model to remember",{},{"id":706,"data":1321,"type":420,"tunes":1347},{"rows":1322,"title":1341,"layout":359,"columns":1342},[1323,1326,1329,1332,1335,1338],{"id":710,"label":1324,"values":1325},"Person thinking",[399,399],{"id":714,"label":1327,"values":1328},"Finding a reference book",[399,399],{"id":718,"label":1330,"values":1331},"Books on the shelf",[399,399],{"id":722,"label":1333,"values":1334},"Current dashboard or instrument panel",[399,399],{"id":726,"label":1336,"values":1337},"Notes from earlier meetings",[399,399],{"id":730,"label":1339,"values":1340},"Doing something in the real world",[399,399],"Think of an AI system like a person at a desk",[1343,1345],{"id":229,"label":1344},"Analogy",{"id":738,"label":1346},"AI system",{},{"id":742,"data":1349,"type":226,"tunes":1352},{"body":1350,"title":1351,"variant":293},"\u003Cstrong>LLM = brain.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>RAG = librarian.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Knowledge base = library.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>State = what the dashboard says right now.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Tools = the hands that can actually do something.\u003C\u002Fstrong>","If you remember only this",{},{"id":748,"data":1354,"type":42,"tunes":1356},{"text":1355,"level":238},"Conclusion",{},{"id":753,"data":1358,"type":218,"tunes":1360},{"text":1359},"RAG is much less mysterious once the parts are separated.",{},{"id":758,"data":1362,"type":218,"tunes":1364},{"text":1363},"The LLM understands and generates language. The application maintains current state. The knowledge base stores information. RAG finds the useful part of that information and puts it into the LLM's context. Tools or the application perform real actions.",{},{"id":763,"data":1366,"type":218,"tunes":1368},{"text":1367},"That is the basic architecture behind many modern AI assistants and agents.",{},{"id":768,"data":1370,"type":42,"tunes":1372},{"text":1371,"level":238},"FAQ",{},{"id":773,"data":1374,"type":773,"tunes":1395},{"items":1375,"title":1394},[1376,1379,1382,1385,1388,1391],{"id":777,"answer":1377,"question":1378},"RAG is a step where an AI searches a knowledge source for relevant information before the language model writes its answer.","What is RAG in simple terms?",{"id":781,"answer":1380,"question":1381},"No. The knowledge base can be completely local on your computer or server.","Does RAG need the Internet?",{"id":785,"answer":1383,"question":1384},"No. The database or files contain the information. RAG is the retrieval process that finds the useful part and gives it to the LLM.","Is RAG the same as a database?",{"id":789,"answer":1386,"question":1387},"No. Memory usually stores previous interactions or events. RAG retrieves relevant knowledge when it is needed.","Is RAG the same as memory?",{"id":793,"answer":1389,"question":1390},"Not necessarily. Current state is usually obtained directly from the application or a state store. RAG is better understood as retrieval from a knowledge source.","Is current application state part of RAG?",{"id":797,"answer":1392,"question":1393},"No. It can provide better evidence, but retrieval can still be wrong or outdated and the LLM can still reason incorrectly.","Does RAG make AI answers correct?","RAG in plain English",{},{"id":803,"data":1397,"type":42,"tunes":1399},{"text":1398,"level":238},"Glossary",{},{"id":808,"data":1401,"type":808,"tunes":1416},{"title":1402,"entries":1403},"The basic terms",[1404,1406,1408,1410,1412,1414],{"term":569,"anchor":813,"definition":1405},"A language model that understands and generates text and can reason over information placed in its context.",{"term":572,"anchor":816,"definition":1407},"Retrieval-Augmented Generation: retrieving relevant external information and adding it to the model's context before generating an answer.",{"term":1211,"anchor":819,"definition":1409},"The files, documents, records or other information that retrieval can search.",{"term":1214,"anchor":418,"definition":1411},"The current facts of an application, system or world at a particular moment.",{"term":1223,"anchor":824,"definition":1413},"The information currently supplied to the language model for one request or reasoning step.",{"term":827,"anchor":828,"definition":1415},"A numerical representation of meaning that can help semantic search find conceptually similar information.",{},{"id":832,"data":1418,"type":42,"tunes":1420},{"text":1419,"level":238},"Primary sources",{},{"id":837,"data":1422,"type":844,"tunes":1427},{"link":839,"meta":1423},{"image":1424,"title":1425,"description":1426},{"url":399},"OpenAI — Vector Store Files","Official documentation showing how files can be attached to vector stores, chunked and made available to file-search retrieval.",{},{"id":847,"data":1429,"type":844,"tunes":1434},{"link":849,"meta":1430},{"image":1431,"title":1432,"description":1433},{"url":399},"OpenAI — Developer Quickstart","Official OpenAI documentation describing tools such as file search for giving models access to external information.",{},{"id":856,"data":1436,"type":844,"tunes":1441},{"link":858,"meta":1437},{"image":1438,"title":1439,"description":1440},{"url":399},"NVIDIA Developer — ACE for Games","Official NVIDIA documentation describing separate Agent, Chat and RAG APIs for connecting game characters to game state, contextual knowledge and model-driven actions.",{},{"id":865,"data":1443,"type":844,"tunes":1448},{"link":867,"meta":1444},{"image":1445,"title":1446,"description":1447},{"url":399},"NVIDIA Developer — How KRAFTON Built PUBG Ally","Official technical explanation separating live match state from knowledge lookup and language-model reasoning.",{},"2.31.6","RAG sounds complicated, but the idea is simple: before an AI answers, it first looks up useful information from a knowledge source and gives that information to the language model. This guide explains RAG, LLMs, state, memory and tools using one simple mental 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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":1816,"slug":1817,"title":1818,"excerpt":1819,"featuredImage":1820,"publishedAt":1821},"466","the-gpu-is-not-the-product-future-proof-private-ai-architecture","La GPU no es el producto: arquitectura de IA privada a prueba de futuro","La infraestructura de IA privada no debe diseñarse en torno a una sola GPU o un solo modelo. Un enfoque más resiliente combina GPUs de inferencia rápida, sistemas de IA ricos en memoria, nodos de IA física y modelos en la nube frontier opcionales detrás de una capa de enrutamiento consciente de las capacidades.","\u002Fuploads\u002F2026\u002F09\u002Fthe-gpu-is-not-the-product-future-proof-private-ai-architecture-1790140878812-8hsl39.webp","2026-09-23T01:19:00.000Z",{"id":1823,"slug":1824,"title":1825,"excerpt":1826,"featuredImage":1827,"publishedAt":1828},"468","ai-agent-memory-is-not-rag-how-to-separate-memory-retrieval-state-and-context","La memoria del agente de IA no es RAG: cómo separar memoria, recuperación, estado y contexto","La memoria del agente, RAG, el estado y el contexto a menudo se usan como si fueran intercambiables. No lo son. 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":1830,"slug":1831,"title":1832,"excerpt":1833,"featuredImage":1834,"publishedAt":1835},"469","rag-failed-but-which-layer-actually-failed-a-diagnostic-method","RAG falló — ¿Pero qué capa falló realmente? Un método de diagnóstico","Cuando una respuesta RAG es incorrecta, culpar a la recuperación o al modelo es demasiado vago. Este método de diagnóstico aísla la cobertura de fuentes, la construcción de consultas, la recuperación, el ranking, el ensamblaje del contexto, la generación, la atribución de evidencia y la actualidad, de modo que el fallo real puede reproducirse y corregirse.","\u002Fuploads\u002F2026\u002F09\u002Frag-failed-but-which-layer-actually-failed-a-diagnostic-method-1790350847177-pior4c.webp","2026-09-24T19:39:00.000Z",{"id":1837,"slug":1838,"title":1839,"excerpt":1840,"featuredImage":1841,"publishedAt":1842},"473","openai-agents-api-vs-agents-sdk-vs-responses-api-what-should-you-build-on-in-2026","OpenAI Agents API vs Agents SDK vs Responses API: ¿Sobre qué deberías construir en 2026?","El stack de agentes de OpenAI cambió en septiembre de 2026. Esta guía de arquitectura separa la Agents API, el Agents SDK, la Responses API y el Codex SDK por propiedad del runtime—para que los equipos puedan elegir el límite de control adecuado en lugar de comparar nombres de productos.","\u002Fuploads\u002F2026\u002F09\u002Fopenai-agents-api-vs-agents-sdk-vs-responses-api-what-should-you-build-on-in-2026-1790351846714-zi7lus.webp","2026-09-25T11:56:00.000Z",{"id":1844,"slug":1845,"title":1846,"excerpt":1847,"featuredImage":1848,"publishedAt":1849},"460","ai-agent-reliability-why-the-final-answer-is-not-enough","Fiabilidad de los Agentes de IA: Por Qué la Respuesta Final No es Suficiente","Una salida correcta no demuestra un razonamiento correcto, una ejecución segura ni un sistema confiable.","\u002Fuploads\u002F2026\u002F09\u002Fai-agent-reliability-why-the-final-answer-is-not-enough-1788955466306-pl0qhz.webp","2026-09-09T04:01:00.000Z",{"id":1851,"slug":1852,"title":1853,"excerpt":1854,"featuredImage":1855,"publishedAt":1856},"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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