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дигитализацију","\u002Fportfolio\u002Fdigitalisierungsportal-archiv-museum-bibliothek-ead-lido-mets-mods",[],{"statusCode":4,"data":206,"message":2227},{"id":207,"title":208,"slug":209,"content":210,"contentJson":211,"excerpt":1045,"featuredImage":1046,"featuredImageAlt":1047,"featuredImageCaption":10,"featuredImageTitle":10,"featuredImageCopyright":10,"featuredImageAuthor":10,"featuredImageSourceUrl":10,"featuredImageLicense":10,"featuredImageIsAiGenerated":43,"status":1048,"publishedAt":1049,"createdAt":1050,"updatedAt":1051,"seoLocalePaths":1052,"categories":1061,"author":1085,"translations":1090},"480","Quand une IA devrait-elle cesser de faire confiance à ses propres connaissances ? — Le déclencheur de récupération","when-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u003Ch2 id=\"section-1\">Question\u003C\u002Fh2>\n\u003Cp>Quand une IA devrait-elle cesser de s'appuyer sur ce qu'elle sait déjà et récupérer des informations externes avant de répondre ?\u003C\u002Fp>\n\u003Cp>Cette question semble simple, mais elle se situe au cœur de l'une des décisions de conception les plus importantes des systèmes d'IA modernes.\u003C\u002Fp>\n\u003Cp>Les grands modèles de langage contiennent des connaissances substantielles dans leurs paramètres. La génération augmentée par récupération ajoute des informations externes à l'exécution. Mais aucun des deux extrêmes n'est idéal.\u003C\u002Fp>\n\u003Cp>Toujours faire confiance au modèle peut produire des réponses obsolètes ou non étayées. Toujours récupérer des informations ajoute de la latence, du coût, un contexte non pertinent et de nouvelles possibilités d'erreurs de récupération.\u003C\u002Fp>\n\u003Cp>Le véritable problème se situe donc avant le RAG : quand la récupération doit-elle avoir lieu ?\u003C\u002Fp>\n\u003Cp>Cet article utilise le terme Déclencheur de récupération pour cette décision. Le Déclencheur de récupération n'est pas présenté ici comme un terme normalisé issu de la littérature de recherche. C'est un concept système pratique qui rassemble des idées déjà visibles dans la recherche sur la récupération active, adaptative et auto-réflexive.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Un Déclencheur de récupération est une condition indiquant qu'un système d'IA devrait cesser de s'appuyer uniquement sur les connaissances internes du modèle et obtenir des preuves externes avant de produire ou de finaliser une réponse.\u003Ccite class=\"block mt-2 text-sm\">— Définition de travail\u003C\u002Fcite>\u003C\u002Fblockquote>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Contenu\">\u003Cstrong class=\"editorjs-toc__title\">Contenu\u003C\u002Fstrong>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-0\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-1\" class=\"editorjs-toc__link\">Question\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-10\" class=\"editorjs-toc__link\">Ce que cela signifie vraiment\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-20\" class=\"editorjs-toc__link\">Exemple le plus simple\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-33\" class=\"editorjs-toc__link\">Où l'exemple cesse de fonctionner\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-43\" class=\"editorjs-toc__link\">Réponse directe\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-48\" class=\"editorjs-toc__link\">Pourquoi il en est ainsi\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-55\" class=\"editorjs-toc__link\">Contexte\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-65\" class=\"editorjs-toc__link\">Hypothèses\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-71\" class=\"editorjs-toc__link\">Variables\u003C\u002Fa>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-1\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-73\" class=\"editorjs-toc__link\">Fraîcheur\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-75\" class=\"editorjs-toc__link\">Spécificité\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-77\" class=\"editorjs-toc__link\">Exigence de preuve\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-79\" class=\"editorjs-toc__link\">Couverture des connaissances\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-81\" class=\"editorjs-toc__link\">Conséquence d'une erreur\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-84\" class=\"editorjs-toc__link\">Méthode de diagnostic \u002F de décision\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-96\" class=\"editorjs-toc__link\">Preuves\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-104\" class=\"editorjs-toc__link\">Exemples réels\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-119\" class=\"editorjs-toc__link\">Idées fausses courantes et modes de défaillance\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-125\" class=\"editorjs-toc__link\">Cas limites\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-136\" class=\"editorjs-toc__link\">Limites\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-143\" class=\"editorjs-toc__link\">Qu'est-ce qui changerait cette réponse ?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-149\" class=\"editorjs-toc__link\">Conclusion\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-157\" class=\"editorjs-toc__link\">Sources Primaires\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-10\">Ce que cela signifie vraiment\u003C\u002Fh2>\n\u003Cp>Un LLM dispose de deux manières fondamentalement différentes d'obtenir des informations.\u003C\u002Fp>\n\u003Cp>La première est la connaissance du modèle. Il s'agit d'informations représentées dans les paramètres appris du modèle. Aucune requête de base de données, recherche web ou consultation de documents n'est nécessaire à l'exécution.\u003C\u002Fp>\n\u003Cp>La seconde est la connaissance à l'exécution. Il s'agit d'informations fournies pendant que le modèle fonctionne : résultats de recherche, enregistrements de base de données, documents, API, fichiers utilisateur, sorties d'outils ou autres preuves récupérées.\u003C\u002Fp>\n\u003Cp>Le RAG relie ces deux mondes. Mais le RAG lui-même ne répond pas à la question de savoir quand cette connexion doit être activée. C'est le rôle du Déclencheur de récupération.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Question\n   ↓\nModel Knowledge\n   ↓\nIs internal knowledge sufficient?\n   ↓\nRetrieval Trigger\n   ↓\nExternal Retrieval, if required\n   ↓\nEvidence\n   ↓\nReasoning\n   ↓\nAnswer Validity Boundary\n   ↓\nAnswer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Le Déclencheur de récupération se situe donc avant la récupération. La Limite de validité de la réponse intervient plus tard.\u003C\u002Fp>\n\u003Cp>La première demande : Ai-je besoin de preuves externes ?\u003C\u002Fp>\n\u003Cp>La seconde demande : Ai-je maintenant suffisamment de preuves pour étayer cette réponse ?\u003C\u002Fp>\n\u003Cp>Ce sont des décisions liées, mais ce ne sont pas la même décision.\u003C\u002Fp>\n\u003Ch2 id=\"section-20\">Exemple le plus simple\u003C\u002Fh2>\n\u003Cp>Considérez trois questions.\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\">Question\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Connaissance interne\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Déclencheur de récupération\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Quelle est la capitale de la France ?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Généralement suffisant\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Pas de déclencheur fort\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Quel est le cours actuel de l'action NVIDIA ?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Potentiellement obsolète\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Déclencher la récupération\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Ce nouvel article scientifique prouve-t-il que X cause Y ?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Impossible d'établir l'affirmation sans examiner les preuves\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Fort déclencheur de récupération\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Cp>La première question repose sur un fait très stable.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>User\n↓\n"What is the capital of France?"\n\nModel knowledge\n↓\nParis\n\nFresh external evidence required?\n↓\nNo\n\nAnswer\n↓\nParis\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Récupérer des documents avant de répondre n'apporterait généralement que peu de valeur.\u003C\u002Fp>\n\u003Cp>Considérez maintenant une question dont la réponse change continuellement.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>User\n↓\n"What is the current NVIDIA stock price?"\n\nModel knowledge\n↓\nPotentially outdated\n\nCurrent information required?\n↓\nYes\n\nRETRIEVAL TRIGGER\n↓\nMarket data \u002F search \u002F API\n↓\nAnswer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Le modèle peut en savoir beaucoup sur NVIDIA. Cela ne signifie pas qu'il connaît le prix actuel.\u003C\u002Fp>\n\u003Cp>Le troisième exemple est encore plus important.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>User\n↓\n"Does this new scientific paper prove that X causes Y?"\n\nModel knowledge\n↓\nCan reason about causality,\nstatistics and scientific methodology.\n\nBut:\nthe actual evidence is not available internally.\n\nRETRIEVAL TRIGGER\n↓\nRetrieve the paper\n↓\nInspect methodology\n↓\nInspect results\n↓\nCompare claim with evidence\n↓\nAnswer Validity Boundary\n↓\nAnswer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>La capacité de raisonnement du modèle peut être parfaitement utile. L'élément manquant est la preuve.\u003C\u002Fp>\n\u003Cp>Cette distinction est fondamentale.\u003C\u002Fp>\n\u003Ch2 id=\"section-33\">Où l'exemple cesse de fonctionner\u003C\u002Fh2>\n\u003Cp>Les exemples ci-dessus font apparaître la décision comme binaire : récupérer ou ne pas récupérer.\u003C\u002Fp>\n\u003Cp>Les systèmes réels sont plus compliqués. Une question peut contenir plusieurs affirmations, certaines stables et d'autres actuelles. Les documents récupérés peuvent être contradictoires. Un récupérateur peut renvoyer des informations non pertinentes. L'information pertinente peut exister mais ne pas être classée assez haut. Un document peut être faisant autorité mais obsolète.\u003C\u002Fp>\n\u003Cp>La récupération elle-même peut également introduire un contexte incorrect dans une réponse par ailleurs raisonnable.\u003C\u002Fp>\n\u003Cp>C'est pourquoi la récupération ne doit pas être traitée comme un synonyme automatique de vérité.\u003C\u002Fp>\n\u003Cp>La recherche sur la récupération adaptative s'est de plus en plus éloignée de l'hypothèse selon laquelle chaque requête devrait recevoir la même stratégie de récupération.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa>, par exemple, explore explicitement la récupération à la demande plutôt que de récupérer de manière indiscriminée un nombre fixe de passages pour chaque entrée. Les auteurs discutent de la manière dont une récupération inutile ou non pertinente peut réduire la qualité des réponses.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> sélectionne de même entre l'absence de récupération, la récupération en une étape et des stratégies de récupération plus complexes selon la complexité de la question.\u003C\u002Fp>\n\u003Cp>La question importante n'est donc pas : Ce système dispose-t-il de RAG ?\u003C\u002Fp>\n\u003Cp>C'est : Ce système peut-il reconnaître quand la récupération est nécessaire et quel type de récupération est approprié ?\u003C\u002Fp>\n\u003Ch2 id=\"section-43\">Réponse directe\u003C\u002Fh2>\n\u003Cp>Une IA devrait déclencher la récupération lorsque la réponse nécessite des informations que les connaissances internes de son modèle ne peuvent pas fournir de manière sûre avec la fraîcheur, la spécificité, la provenance ou le support probatoire requis.\u003C\u002Fp>\n\u003Cp>Dans les systèmes pratiques, un déclencheur de récupération peut émerger de plusieurs conditions :\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Need for current information\n        OR\nNeed for exact source-specific information\n        OR\nNeed for evidence or provenance\n        OR\nNeed for private\u002Fuser-specific information\n        OR\nInsufficient knowledge coverage\n        OR\nConflicting evidence\n        OR\nHigh consequence of factual error\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Si aucune de ces conditions n'est matériellement présente, la récupération peut être inutile. Si une ou plusieurs sont présentes, les preuves externes font partie du processus de génération de la réponse.\u003C\u002Fp>\n\u003Ch2 id=\"section-48\">Pourquoi il en est ainsi\u003C\u002Fh2>\n\u003Cp>Les connaissances internes d'un modèle de langage sont souvent décrites comme des connaissances paramétriques. Elles ont été apprises pendant l'entraînement et encodées dans les paramètres du modèle.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">Le travail original de Lewis et al. sur le RAG\u003C\u002Fa> a présenté la récupération comme une combinaison de cette mémoire paramétrique avec une mémoire externe non paramétrique. La mémoire externe peut être recherchée et mise à jour sans réentraîner l'ensemble du modèle de langage.\u003C\u002Fp>\n\u003Cp>Cette distinction crée un problème systémique inévitable.\u003C\u002Fp>\n\u003Cp>Le modèle peut savoir des choses. Mais le modèle ne peut pas supposer que tout ce qu'il sait est actuel, complet, suffisamment spécifique et soutenu par les preuves requises.\u003C\u002Fp>\n\u003Cp>Un modèle peut donc produire une réponse linguistiquement convaincante tout en opérant au-delà du point où ses connaissances internes sont suffisantes.\u003C\u002Fp>\n\u003Cp>C'est à ce point qu'un déclencheur de récupération devient utile.\u003C\u002Fp>\n\u003Ch2 id=\"section-55\">Contexte\u003C\u002Fh2>\n\u003Cp>Le RAG traditionnel ressemble souvent à ceci :\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Question\n↓\nRetrieve documents\n↓\nAdd documents to context\n↓\nGenerate answer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Cette architecture suppose une récupération avant la génération. Cela fonctionne bien pour de nombreuses applications à forte intensité de connaissances, mais elle peut aussi effectuer une récupération inutile.\u003C\u002Fp>\n\u003Cp>Des approches plus avancées introduisent une étape adaptative :\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Question\n↓\nEvaluate information requirement\n↓\n        ┌───────────────┐\n        │               │\n   no retrieval      retrieval\n        │               │\n        ↓               ↓\n model knowledge    external evidence\n        │               │\n        └───────┬───────┘\n                ↓\n              answer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> va plus loin en considérant la récupération pendant la génération elle-même. Il utilise la génération à venir et les tokens à faible confiance comme signaux pour récupérer des informations supplémentaires.\u003C\u002Fp>\n\u003Cp>Self-RAG introduit de même des mécanismes permettant à la récupération, la génération et la critique d'interagir au lieu de traiter la récupération comme une étape de prétraitement inconditionnelle.\u003C\u002Fp>\n\u003Cp>Adaptive-RAG aborde le même problème plus large sous l'angle de la complexité des requêtes : différentes questions peuvent nécessiter différentes stratégies de récupération.\u003C\u002Fp>\n\u003Cp>Ces approches diffèrent techniquement. Mais elles révèlent la même intuition architecturale : la récupération devrait être une décision, pas simplement un interrupteur permanent.\u003C\u002Fp>\n\u003Ch2 id=\"section-65\">Hypothèses\u003C\u002Fh2>\n\u003Cp>Le cadre Retrieval Trigger suppose qu'un système a accès à au moins une source d'information externe lorsque la récupération est nécessaire.\u003C\u002Fp>\n\u003Cp>Cette source pourrait être une recherche web, un stockage de documents, une base de données vectorielle, une base de données SQL, un graphe de connaissances, une API, un système d'entreprise, un document téléchargé par l'utilisateur ou une sortie d'outil.\u003C\u002Fp>\n\u003Cp>Il suppose également que la récupération a un coût. Ce coût n'est pas nécessairement financier.\u003C\u002Fp>\n\u003Cp>La récupération introduit de la latence, une consommation de tokens, une utilisation du contexte, une complexité d'infrastructure et la possibilité de récupérer des informations trompeuses.\u003C\u002Fp>\n\u003Cp>Le système optimal ne maximise donc pas la récupération. Il maximise la récupération appropriée.\u003C\u002Fp>\n\u003Ch2 id=\"section-71\">Variables\u003C\u002Fh2>\n\u003Cp>Un Retrieval Trigger pratique peut considérer cinq variables principales.\u003C\u002Fp>\n\u003Ch3 id=\"section-73\">Fraîcheur\u003C\u002Fh3>\n\u003Cp>Quelle est la probabilité que l'information requise ait changé ? La capitale de la France a une très faible volatilité. Le cours d'une action a une volatilité extrêmement élevée.\u003C\u002Fp>\n\u003Ch3 id=\"section-75\">Spécificité\u003C\u002Fh3>\n\u003Cp>La question nécessite-t-elle des informations provenant d'une source, d'un document, d'une organisation, d'un compte ou d'un ensemble de données particulier ? Si l'utilisateur demande ce que dit un contrat spécifique, les connaissances générales du modèle sont non pertinentes. Le contrat doit être récupéré.\u003C\u002Fp>\n\u003Ch3 id=\"section-77\">Exigence de preuve\u003C\u002Fh3>\n\u003Cp>La réponse a-t-elle besoin d'une provenance ? Un modèle peut savoir qu'une affirmation est généralement acceptée mais avoir tout de même besoin d'une source lorsque la tâche exige une vérification.\u003C\u002Fp>\n\u003Ch3 id=\"section-79\">Couverture des connaissances\u003C\u002Fh3>\n\u003Cp>Le sujet est-il susceptible d'être représenté adéquatement dans les connaissances internes du modèle ? Des informations rares, propriétaires, très locales ou nouvellement publiées créent une pression de récupération plus forte.\u003C\u002Fp>\n\u003Ch3 id=\"section-81\">Conséquence d'une erreur\u003C\u002Fh3>\n\u003Cp>Toutes les réponses incorrectes n'ont pas le même impact. Lorsque l'exactitude factuelle affecte matériellement une décision, le seuil de preuve acceptable peut être plus élevé.\u003C\u002Fp>\n\u003Cp>Ces variables n'ont pas besoin d'être implémentées sous forme de scores numériques littéraux. Elles décrivent la surface de décision.\u003C\u002Fp>\n\u003Ch2 id=\"section-84\">Méthode de diagnostic \u002F de décision\u003C\u002Fh2>\n\u003Cp>Un déclencheur de récupération très simple peut être implémenté sans apprentissage automatique.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>def should_retrieve(\n    time_sensitive=False,\n    source_specific=False,\n    evidence_required=False,\n    private_context=False,\n    knowledge_uncertain=False,\n    conflicting_information=False\n):\n    return any([\n        time_sensitive,\n        source_specific,\n        evidence_required,\n        private_context,\n        knowledge_uncertain,\n        conflicting_information,\n    ])\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Pour une question factuelle stable :\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>should_retrieve()\n# False\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Pour un cours actuel :\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>should_retrieve(\n    time_sensitive=True\n)\n# True\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Pour une affirmation scientifique :\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>should_retrieve(\n    source_specific=True,\n    evidence_required=True\n)\n# True\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Les systèmes de production peuvent rendre cette décision bien plus sophistiquée. Un classifieur pourrait prédire les besoins de récupération. Un modèle pourrait émettre des jetons de contrôle spéciaux. Un routeur pourrait classifier la complexité de la requête. La récupération pourrait également être déclenchée de manière répétée pendant la génération.\u003C\u002Fp>\n\u003Cp>L'implémentation peut changer. La question architecturale reste la même :\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Les preuves actuellement disponibles pour le modèle sont-elles suffisantes pour la réponse qu'il s'apprête à produire ?\u003C\u002Fblockquote>\n\u003Ch2 id=\"section-96\">Preuves\u003C\u002Fh2>\n\u003Cp>Le concept proposé ici est cohérent avec plusieurs axes de recherche sur la récupération.\u003C\u002Fp>\n\u003Cp>L'\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">architecture RAG\u003C\u002Fa> originale a démontré l'utilité de combiner les connaissances paramétriques d'un modèle avec des connaissances externes non paramétriques, en particulier pour les tâches à forte intensité de connaissances.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> explore explicitement la récupération active pendant la génération, y compris la récupération déclenchée par un contenu à venir de faible confiance.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa> démontre une architecture dans laquelle la récupération peut se produire à la demande et est suivie d'une réflexion sur les passages récupérés et le contenu généré.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> choisit dynamiquement parmi différentes stratégies selon la complexité de la question, y compris les situations où aucune récupération n'est nécessaire.\u003C\u002Fp>\n\u003Cp>Le terme Retrieval Trigger est utilisé ici comme une abstraction au niveau du système pour cette famille plus large de décisions.\u003C\u002Fp>\n\u003Cp>Il ne prétend pas que ces articles utilisent la même terminologie. Il identifie plutôt le problème architectural commun : qu'est-ce qui amène un système d'IA à passer de connaissances internes à des preuves externes ?\u003C\u002Fp>\n\u003Ch2 id=\"section-104\">Exemples réels\u003C\u002Fh2>\n\u003Cp>Considérez un assistant de support connecté à la documentation d'une entreprise.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"How do I reset my password?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Si la procédure est stable et représentée de manière fiable dans les instructions actuelles de l'assistant, une réponse directe peut être appropriée.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"What permissions does my account currently have?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Cette information est spécifique à l'utilisateur et dynamique. Le déclencheur de récupération se déclenche. Le système doit inspecter les données réelles du compte ou d'autorisation.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"Why was my production deployment rejected yesterday?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Le modèle peut comprendre les systèmes de déploiement et expliquer les raisons courantes. Mais la question porte sur un événement particulier. Les journaux, la sortie CI\u002FCD ou les enregistrements d'incidents sont nécessaires.\u003C\u002Fp>\n\u003Cp>La même logique s'applique à la recherche web.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"What is RAG?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Une explication générale peut ne pas nécessiter de récupération.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"What did the authors of Self-RAG specifically conclude about unnecessary retrieval?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Maintenant, des preuves spécifiques à la source sont requises.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"What is the latest research on adaptive retrieval?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Cela introduit également une exigence de fraîcheur. Le sujet sous-jacent n'a pas changé. L'exigence d'information a changé.\u003C\u002Fp>\n\u003Ch2 id=\"section-119\">Idées fausses courantes et modes de défaillance\u003C\u002Fh2>\n\u003Cp>Plus de récupération produit automatiquement une meilleure réponse. Ce n'est pas le cas. Les documents non pertinents consomment du contexte et peuvent distraire la génération.\u003C\u002Fp>\n\u003Cp>Une confiance élevée du modèle signifie que la récupération est inutile. Un modèle peut produire une réponse incorrecte avec assurance. La confiance auto-déclarée ne doit donc pas être traitée comme le seul déclencheur.\u003C\u002Fp>\n\u003Cp>Une récupération réussie signifie que la réponse est vérifiée. La récupération ne fournit que des preuves candidates. Les preuves doivent encore être pertinentes, suffisamment faisant autorité et correctement interprétées.\u003C\u002Fp>\n\u003Cp>Le RAG résout automatiquement les connaissances obsolètes. Il ne le fait que si le corpus de récupération lui-même contient des informations à jour. Récupérer un document obsolète ne crée pas une réponse actuelle.\u003C\u002Fp>\n\u003Cp>Une étape de récupération suffit toujours. Les questions complexes peuvent nécessiter plusieurs éléments de preuve ou une récupération itérative.\u003C\u002Fp>\n\u003Ch2 id=\"section-125\">Cas limites\u003C\u002Fh2>\n\u003Cp>Certaines questions contiennent à la fois des informations stables et instables.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"Who founded NVIDIA, and what is its market capitalization today?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>La première partie peut être résolue à partir des connaissances stables du modèle. La seconde partie nécessite des informations actuelles.\u003C\u002Fp>\n\u003Cp>Un système suffisamment capable ne devrait pas nécessairement traiter l'ensemble de la requête comme une seule décision de récupération. Il peut déclencher la récupération uniquement là où c'est nécessaire.\u003C\u002Fp>\n\u003Cp>Un autre cas limite est le désaccord entre les sources. Supposons que la récupération renvoie trois documents contenant des affirmations incompatibles.\u003C\u002Fp>\n\u003Cp>Le Déclencheur de Récupération a déjà réussi : le système a reconnu que des preuves externes étaient nécessaires. Mais la tâche n'est pas terminée.\u003C\u002Fp>\n\u003Cp>Le système est maintenant confronté à un problème d'évaluation des preuves. C'est là que la Frontière de Validité de la Réponse devient importante.\u003C\u002Fp>\n\u003Cp>Le système peut avoir récupéré des informations et ne pas encore posséder suffisamment de preuves pour tirer une conclusion solide.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Retrieval Trigger\n≠\npermission to answer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Le déclencheur obtient des preuves. La frontière de validité détermine si ces preuves sont suffisantes.\u003C\u002Fp>\n\u003Ch2 id=\"section-136\">Limites\u003C\u002Fh2>\n\u003Cp>Le Déclencheur de Récupération est un cadre conceptuel, pas un algorithme universel.\u003C\u002Fp>\n\u003Cp>Différents systèmes nécessiteront différentes règles de déclenchement. Un bot de support client, un assistant de recherche scientifique, un moteur de recherche et un agent logiciel autonome n'ont pas des exigences de preuves identiques.\u003C\u002Fp>\n\u003Cp>Les seuils de déclenchement peuvent également créer leurs propres modes de défaillance. Un seuil trop bas provoque une récupération excessive. Un seuil trop élevé provoque des réponses non étayées.\u003C\u002Fp>\n\u003Cp>L'infrastructure de récupération elle-même compte également. Un déclencheur parfait connecté à une mauvaise collection de sources produit toujours des preuves médiocres.\u003C\u002Fp>\n\u003Cp>De même, une excellente base de connaissances offre peu de valeur si le déclencheur ne s'active jamais lorsqu'il est nécessaire.\u003C\u002Fp>\n\u003Cp>Le Déclencheur de Récupération ne résout donc qu'une partie d'une architecture plus vaste.\u003C\u002Fp>\n\u003Ch2 id=\"section-143\">Qu'est-ce qui changerait cette réponse ?\u003C\u002Fh2>\n\u003Cp>Les futurs modèles pourraient contenir de meilleurs mécanismes pour identifier leurs propres limites de connaissances. Les récupérateurs pourraient devenir moins chers et plus rapides. Les systèmes à long contexte pourraient transporter beaucoup plus de matériel source en continu.\u003C\u002Fp>\n\u003Cp>Les modèles peuvent aussi de plus en plus combiner recherche, bases de données, outils et connaissances structurées sans exposer une étape RAG distincte au développeur d'application.\u003C\u002Fp>\n\u003Cp>Ces changements pourraient modifier la façon dont le déclencheur est implémenté. Ils ne suppriment pas nécessairement la décision sous-jacente.\u003C\u002Fp>\n\u003Cp>Tant qu'il existe une différence entre les informations déjà disponibles pour le modèle et les informations qui doivent être obtenues à l'extérieur, un système a encore besoin d'un mécanisme pour déterminer quand franchir cette frontière.\u003C\u002Fp>\n\u003Cp>L'implémentation peut disparaître de la vue. La question architecturale demeure.\u003C\u002Fp>\n\u003Ch2 id=\"section-149\">Conclusion\u003C\u002Fh2>\n\u003Cp>Le RAG commence trop tard pour expliquer tout le problème.\u003C\u002Fp>\n\u003Cp>Avant que la récupération puisse avoir lieu, un système d'IA doit déterminer si la récupération est nécessaire. Cette décision est le Déclencheur de Récupération.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Stable known fact\n→ answer from model knowledge\n\nCurrent fact\n→ retrieve\n\nSource-specific or evidence-dependent claim\n→ retrieve and verify\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Mais l'implication plus large est plus importante. Une IA fiable n'a pas seulement besoin d'accéder à la connaissance. Elle a besoin d'une méthode pour déterminer quand sa connaissance actuelle est insuffisante.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Model Knowledge\n        ↓\nRetrieval Trigger\n        ↓\nRuntime Knowledge \u002F RAG\n        ↓\nEvidence\n        ↓\nReasoning\n        ↓\nAnswer Validity Boundary\n        ↓\nAnswer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Le Déclencheur de Récupération détermine quand le système doit chercher des preuves. La Frontière de Validité de la Réponse détermine si ces preuves sont suffisantes.\u003C\u002Fp>\n\u003Cp>Ensemble, ils décrivent quelque chose de plus utile que le RAG seul : un processus de décision pour passer de ce qu'une IA semble savoir à ce qu'elle peut réellement soutenir.\u003C\u002Fp>\n\u003Ch2 id=\"section-157\">Sources Primaires\u003C\u002Fh2>\n\u003Cp>Patrick Lewis et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\u003C\u002Fa> (2020). Travail fondateur sur le RAG décrivant la combinaison de la mémoire paramétrique du modèle avec une mémoire externe non paramétrique.\u003C\u002Fp>\n\u003Cp>Zhengbao Jiang et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">Active Retrieval Augmented Generation\u003C\u002Fa> (2023). Introduit FLARE et la récupération active pendant la génération, y compris la récupération basée sur un contenu prédit à faible confiance.\u003C\u002Fp>\n\u003Cp>Akari Asai et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection\u003C\u002Fa> (2023). Explore la récupération adaptative à la demande et l'auto-réflexion au lieu d'une récupération fixe inconditionnelle.\u003C\u002Fp>\n\u003Cp>Soyeong Jeong et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity\u003C\u002Fa> (2024). Sélectionne dynamiquement entre aucune récupération, une récupération en une étape et des stratégies de récupération plus complexes selon la question entrante.\u003C\u002Fp>",{"time":212,"blocks":213,"version":1044},1790575189729,[214,220,226,231,236,241,246,251,259,266,271,276,281,286,291,297,302,307,312,317,322,327,348,353,358,363,368,373,378,383,388,393,398,403,408,413,418,423,428,433,438,443,448,453,458,463,468,473,478,483,488,493,498,503,508,513,518,523,528,533,538,543,548,553,558,563,568,573,578,583,588,593,598,603,608,613,618,623,628,633,638,643,648,653,658,663,668,673,678,683,688,693,698,703,708,714,719,724,729,734,739,744,749,754,759,764,769,774,779,784,789,794,799,804,809,814,819,824,829,834,839,844,849,854,859,864,869,874,879,884,889,894,899,904,909,914,919,924,929,934,939,944,949,954,959,964,969,974,979,984,989,994,999,1004,1009,1014,1019,1024,1029,1034,1039],{"id":215,"data":216,"type":42,"tunes":219},"Wt7UfNeFlS",{"text":217,"level":218},"Question",2,{},{"id":221,"data":222,"type":224,"tunes":225},"T-ZCQblBzm",{"text":223},"Quand une IA devrait-elle cesser de s'appuyer sur ce qu'elle sait déjà et récupérer des informations externes avant de répondre ?","paragraph",{},{"id":227,"data":228,"type":224,"tunes":230},"vBcd4061WS",{"text":229},"Cette question semble simple, mais elle se situe au cœur de l'une des décisions de conception les plus importantes des systèmes d'IA modernes.",{},{"id":232,"data":233,"type":224,"tunes":235},"r9NZ-Fzw0e",{"text":234},"Les grands modèles de langage contiennent des connaissances substantielles dans leurs paramètres. La génération augmentée par récupération ajoute des informations externes à l'exécution. Mais aucun des deux extrêmes n'est idéal.",{},{"id":237,"data":238,"type":224,"tunes":240},"CpzlgJjAVL",{"text":239},"Toujours faire confiance au modèle peut produire des réponses obsolètes ou non étayées. Toujours récupérer des informations ajoute de la latence, du coût, un contexte non pertinent et de nouvelles possibilités d'erreurs de récupération.",{},{"id":242,"data":243,"type":224,"tunes":245},"yeclJhYJ1a",{"text":244},"Le véritable problème se situe donc avant le RAG : quand la récupération doit-elle avoir lieu ?",{},{"id":247,"data":248,"type":224,"tunes":250},"FgLSWvpZMg",{"text":249},"Cet article utilise le terme Déclencheur de récupération pour cette décision. Le Déclencheur de récupération n'est pas présenté ici comme un terme normalisé issu de la littérature de recherche. C'est un concept système pratique qui rassemble des idées déjà visibles dans la recherche sur la récupération active, adaptative et auto-réflexive.",{},{"id":252,"data":253,"type":257,"tunes":258},"Muzvv-2uzU",{"text":254,"caption":255,"alignment":256},"Un Déclencheur de récupération est une condition indiquant qu'un système d'IA devrait cesser de s'appuyer uniquement sur les connaissances internes du modèle et obtenir des preuves externes avant de produire ou de finaliser une réponse.","Définition de travail","left","quote",{},{"id":260,"data":261,"type":264,"tunes":265},"1BGt1waZ01",{"title":262,"maxLevel":263,"minLevel":218},"Contenu",3,"tableOfContents",{},{"id":267,"data":268,"type":42,"tunes":270},"BFKJ2htjYN",{"text":269,"level":218},"Ce que cela signifie vraiment",{},{"id":272,"data":273,"type":224,"tunes":275},"yfBYqVwObv",{"text":274},"Un LLM dispose de deux manières fondamentalement différentes d'obtenir des informations.",{},{"id":277,"data":278,"type":224,"tunes":280},"Y4JYebztDi",{"text":279},"La première est la connaissance du modèle. Il s'agit d'informations représentées dans les paramètres appris du modèle. Aucune requête de base de données, recherche web ou consultation de documents n'est nécessaire à l'exécution.",{},{"id":282,"data":283,"type":224,"tunes":285},"x2L37FSTBK",{"text":284},"La seconde est la connaissance à l'exécution. Il s'agit d'informations fournies pendant que le modèle fonctionne : résultats de recherche, enregistrements de base de données, documents, API, fichiers utilisateur, sorties d'outils ou autres preuves récupérées.",{},{"id":287,"data":288,"type":224,"tunes":290},"2szDUb7_-4",{"text":289},"Le RAG relie ces deux mondes. Mais le RAG lui-même ne répond pas à la question de savoir quand cette connexion doit être activée. C'est le rôle du Déclencheur de récupération.",{},{"id":292,"data":293,"type":295,"tunes":296},"5_yjTthHV4",{"code":294},"Question\n   ↓\nModel Knowledge\n   ↓\nIs internal knowledge sufficient?\n   ↓\nRetrieval Trigger\n   ↓\nExternal Retrieval, if required\n   ↓\nEvidence\n   ↓\nReasoning\n   ↓\nAnswer Validity Boundary\n   ↓\nAnswer","code",{},{"id":298,"data":299,"type":224,"tunes":301},"rH2K36ambR",{"text":300},"Le Déclencheur de récupération se situe donc avant la récupération. La Limite de validité de la réponse intervient plus tard.",{},{"id":303,"data":304,"type":224,"tunes":306},"L0WlGs_dTF",{"text":305},"La première demande : Ai-je besoin de preuves externes ?",{},{"id":308,"data":309,"type":224,"tunes":311},"JyE4O9aDCW",{"text":310},"La seconde demande : Ai-je maintenant suffisamment de preuves pour étayer cette réponse ?",{},{"id":313,"data":314,"type":224,"tunes":316},"L9JP5xByy4",{"text":315},"Ce sont des décisions liées, mais ce ne sont pas la même décision.",{},{"id":318,"data":319,"type":42,"tunes":321},"4hPbiDSHek",{"text":320,"level":218},"Exemple le plus simple",{},{"id":323,"data":324,"type":224,"tunes":326},"cER32Me6gA",{"text":325},"Considérez trois questions.",{},{"id":328,"data":329,"type":346,"tunes":347},"izi7nU9FE9",{"content":330,"stretched":43,"withHeadings":14},[331,334,338,342],[217,332,333],"Connaissance interne","Déclencheur de récupération",[335,336,337],"Quelle est la capitale de la France ?","Généralement suffisant","Pas de déclencheur fort",[339,340,341],"Quel est le cours actuel de l'action NVIDIA ?","Potentiellement obsolète","Déclencher la récupération",[343,344,345],"Ce nouvel article scientifique prouve-t-il que X cause Y ?","Impossible d'établir l'affirmation sans examiner les preuves","Fort déclencheur de récupération","table",{},{"id":349,"data":350,"type":224,"tunes":352},"cb-Kx0fKs4",{"text":351},"La première question repose sur un fait très stable.",{},{"id":354,"data":355,"type":295,"tunes":357},"MZJzwvZUH7",{"code":356},"User\n↓\n\"What is the capital of France?\"\n\nModel knowledge\n↓\nParis\n\nFresh external evidence required?\n↓\nNo\n\nAnswer\n↓\nParis",{},{"id":359,"data":360,"type":224,"tunes":362},"fRP7-aWTJB",{"text":361},"Récupérer des documents avant de répondre n'apporterait généralement que peu de valeur.",{},{"id":364,"data":365,"type":224,"tunes":367},"O2TaSvLoxO",{"text":366},"Considérez maintenant une question dont la réponse change continuellement.",{},{"id":369,"data":370,"type":295,"tunes":372},"cNv0Dp7Mk3",{"code":371},"User\n↓\n\"What is the current NVIDIA stock price?\"\n\nModel knowledge\n↓\nPotentially outdated\n\nCurrent information required?\n↓\nYes\n\nRETRIEVAL TRIGGER\n↓\nMarket data \u002F search \u002F API\n↓\nAnswer",{},{"id":374,"data":375,"type":224,"tunes":377},"Y3NDw8awnA",{"text":376},"Le modèle peut en savoir beaucoup sur NVIDIA. Cela ne signifie pas qu'il connaît le prix actuel.",{},{"id":379,"data":380,"type":224,"tunes":382},"FwjiaA6mdJ",{"text":381},"Le troisième exemple est encore plus important.",{},{"id":384,"data":385,"type":295,"tunes":387},"G48ZGtX4XK",{"code":386},"User\n↓\n\"Does this new scientific paper prove that X causes Y?\"\n\nModel knowledge\n↓\nCan reason about causality,\nstatistics and scientific methodology.\n\nBut:\nthe actual evidence is not available internally.\n\nRETRIEVAL TRIGGER\n↓\nRetrieve the paper\n↓\nInspect methodology\n↓\nInspect results\n↓\nCompare claim with evidence\n↓\nAnswer Validity Boundary\n↓\nAnswer",{},{"id":389,"data":390,"type":224,"tunes":392},"nGu-KcQC6l",{"text":391},"La capacité de raisonnement du modèle peut être parfaitement utile. L'élément manquant est la preuve.",{},{"id":394,"data":395,"type":224,"tunes":397},"_bUxnOYvHG",{"text":396},"Cette distinction est fondamentale.",{},{"id":399,"data":400,"type":42,"tunes":402},"etbE_esRx4",{"text":401,"level":218},"Où l'exemple cesse de fonctionner",{},{"id":404,"data":405,"type":224,"tunes":407},"0iSdy2Msw7",{"text":406},"Les exemples ci-dessus font apparaître la décision comme binaire : récupérer ou ne pas récupérer.",{},{"id":409,"data":410,"type":224,"tunes":412},"8Go7nm2niJ",{"text":411},"Les systèmes réels sont plus compliqués. Une question peut contenir plusieurs affirmations, certaines stables et d'autres actuelles. Les documents récupérés peuvent être contradictoires. Un récupérateur peut renvoyer des informations non pertinentes. L'information pertinente peut exister mais ne pas être classée assez haut. Un document peut être faisant autorité mais obsolète.",{},{"id":414,"data":415,"type":224,"tunes":417},"8dVjRU5cXg",{"text":416},"La récupération elle-même peut également introduire un contexte incorrect dans une réponse par ailleurs raisonnable.",{},{"id":419,"data":420,"type":224,"tunes":422},"pLqSH5-OJR",{"text":421},"C'est pourquoi la récupération ne doit pas être traitée comme un synonyme automatique de vérité.",{},{"id":424,"data":425,"type":224,"tunes":427},"ww4Od2cmTr",{"text":426},"La recherche sur la récupération adaptative s'est de plus en plus éloignée de l'hypothèse selon laquelle chaque requête devrait recevoir la même stratégie de récupération.",{},{"id":429,"data":430,"type":224,"tunes":432},"1yE2LUP7cF",{"text":431},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa>, par exemple, explore explicitement la récupération à la demande plutôt que de récupérer de manière indiscriminée un nombre fixe de passages pour chaque entrée. Les auteurs discutent de la manière dont une récupération inutile ou non pertinente peut réduire la qualité des réponses.",{},{"id":434,"data":435,"type":224,"tunes":437},"915QBDW89m",{"text":436},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> sélectionne de même entre l'absence de récupération, la récupération en une étape et des stratégies de récupération plus complexes selon la complexité de la question.",{},{"id":439,"data":440,"type":224,"tunes":442},"1FBgxY0QQp",{"text":441},"La question importante n'est donc pas : Ce système dispose-t-il de RAG ?",{},{"id":444,"data":445,"type":224,"tunes":447},"4xdj86u8Qz",{"text":446},"C'est : Ce système peut-il reconnaître quand la récupération est nécessaire et quel type de récupération est approprié ?",{},{"id":449,"data":450,"type":42,"tunes":452},"bGPa0AsJI6",{"text":451,"level":218},"Réponse directe",{},{"id":454,"data":455,"type":224,"tunes":457},"fBKcyJ0IcX",{"text":456},"Une IA devrait déclencher la récupération lorsque la réponse nécessite des informations que les connaissances internes de son modèle ne peuvent pas fournir de manière sûre avec la fraîcheur, la spécificité, la provenance ou le support probatoire requis.",{},{"id":459,"data":460,"type":224,"tunes":462},"JbIPIJjkXK",{"text":461},"Dans les systèmes pratiques, un déclencheur de récupération peut émerger de plusieurs conditions :",{},{"id":464,"data":465,"type":295,"tunes":467},"_JbTSHlrtH",{"code":466},"Need for current information\n        OR\nNeed for exact source-specific information\n        OR\nNeed for evidence or provenance\n        OR\nNeed for private\u002Fuser-specific information\n        OR\nInsufficient knowledge coverage\n        OR\nConflicting evidence\n        OR\nHigh consequence of factual error",{},{"id":469,"data":470,"type":224,"tunes":472},"Aaem6fQ_tF",{"text":471},"Si aucune de ces conditions n'est matériellement présente, la récupération peut être inutile. Si une ou plusieurs sont présentes, les preuves externes font partie du processus de génération de la réponse.",{},{"id":474,"data":475,"type":42,"tunes":477},"x2DDg7Ue1-",{"text":476,"level":218},"Pourquoi il en est ainsi",{},{"id":479,"data":480,"type":224,"tunes":482},"1GTaWG9ViB",{"text":481},"Les connaissances internes d'un modèle de langage sont souvent décrites comme des connaissances paramétriques. Elles ont été apprises pendant l'entraînement et encodées dans les paramètres du modèle.",{},{"id":484,"data":485,"type":224,"tunes":487},"klwNY3lr1d",{"text":486},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">Le travail original de Lewis et al. sur le RAG\u003C\u002Fa> a présenté la récupération comme une combinaison de cette mémoire paramétrique avec une mémoire externe non paramétrique. La mémoire externe peut être recherchée et mise à jour sans réentraîner l'ensemble du modèle de langage.",{},{"id":489,"data":490,"type":224,"tunes":492},"Fyw2AVDbxR",{"text":491},"Cette distinction crée un problème systémique inévitable.",{},{"id":494,"data":495,"type":224,"tunes":497},"4FvbthV3in",{"text":496},"Le modèle peut savoir des choses. Mais le modèle ne peut pas supposer que tout ce qu'il sait est actuel, complet, suffisamment spécifique et soutenu par les preuves requises.",{},{"id":499,"data":500,"type":224,"tunes":502},"asxdihTbcB",{"text":501},"Un modèle peut donc produire une réponse linguistiquement convaincante tout en opérant au-delà du point où ses connaissances internes sont suffisantes.",{},{"id":504,"data":505,"type":224,"tunes":507},"jGgq116uAa",{"text":506},"C'est à ce point qu'un déclencheur de récupération devient utile.",{},{"id":509,"data":510,"type":42,"tunes":512},"T6q_BUeDg3",{"text":511,"level":218},"Contexte",{},{"id":514,"data":515,"type":224,"tunes":517},"9H_bNlyoYs",{"text":516},"Le RAG traditionnel ressemble souvent à ceci :",{},{"id":519,"data":520,"type":295,"tunes":522},"YSZR1AInSj",{"code":521},"Question\n↓\nRetrieve documents\n↓\nAdd documents to context\n↓\nGenerate answer",{},{"id":524,"data":525,"type":224,"tunes":527},"HnzY2Q9xTs",{"text":526},"Cette architecture suppose une récupération avant la génération. Cela fonctionne bien pour de nombreuses applications à forte intensité de connaissances, mais elle peut aussi effectuer une récupération inutile.",{},{"id":529,"data":530,"type":224,"tunes":532},"Aho03YTGAU",{"text":531},"Des approches plus avancées introduisent une étape adaptative :",{},{"id":534,"data":535,"type":295,"tunes":537},"uK0l0tYLg6",{"code":536},"Question\n↓\nEvaluate information requirement\n↓\n        ┌───────────────┐\n        │               │\n   no retrieval      retrieval\n        │               │\n        ↓               ↓\n model knowledge    external evidence\n        │               │\n        └───────┬───────┘\n                ↓\n              answer",{},{"id":539,"data":540,"type":224,"tunes":542},"Upb-15aN8T",{"text":541},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> va plus loin en considérant la récupération pendant la génération elle-même. Il utilise la génération à venir et les tokens à faible confiance comme signaux pour récupérer des informations supplémentaires.",{},{"id":544,"data":545,"type":224,"tunes":547},"I5hs5j9IKc",{"text":546},"Self-RAG introduit de même des mécanismes permettant à la récupération, la génération et la critique d'interagir au lieu de traiter la récupération comme une étape de prétraitement inconditionnelle.",{},{"id":549,"data":550,"type":224,"tunes":552},"mw2jbuWA-g",{"text":551},"Adaptive-RAG aborde le même problème plus large sous l'angle de la complexité des requêtes : différentes questions peuvent nécessiter différentes stratégies de récupération.",{},{"id":554,"data":555,"type":224,"tunes":557},"DUba0EfbWg",{"text":556},"Ces approches diffèrent techniquement. Mais elles révèlent la même intuition architecturale : la récupération devrait être une décision, pas simplement un interrupteur permanent.",{},{"id":559,"data":560,"type":42,"tunes":562},"wzX0jC8H8b",{"text":561,"level":218},"Hypothèses",{},{"id":564,"data":565,"type":224,"tunes":567},"4puAk8h-NF",{"text":566},"Le cadre Retrieval Trigger suppose qu'un système a accès à au moins une source d'information externe lorsque la récupération est nécessaire.",{},{"id":569,"data":570,"type":224,"tunes":572},"Qsm42lc7aC",{"text":571},"Cette source pourrait être une recherche web, un stockage de documents, une base de données vectorielle, une base de données SQL, un graphe de connaissances, une API, un système d'entreprise, un document téléchargé par l'utilisateur ou une sortie d'outil.",{},{"id":574,"data":575,"type":224,"tunes":577},"Rznt7yvqT2",{"text":576},"Il suppose également que la récupération a un coût. Ce coût n'est pas nécessairement financier.",{},{"id":579,"data":580,"type":224,"tunes":582},"wQoEfZuFPe",{"text":581},"La récupération introduit de la latence, une consommation de tokens, une utilisation du contexte, une complexité d'infrastructure et la possibilité de récupérer des informations trompeuses.",{},{"id":584,"data":585,"type":224,"tunes":587},"WM1F9QkT2G",{"text":586},"Le système optimal ne maximise donc pas la récupération. Il maximise la récupération appropriée.",{},{"id":589,"data":590,"type":42,"tunes":592},"Z4gw9SX7jo",{"text":591,"level":218},"Variables",{},{"id":594,"data":595,"type":224,"tunes":597},"Z_sKNO6vmp",{"text":596},"Un Retrieval Trigger pratique peut considérer cinq variables principales.",{},{"id":599,"data":600,"type":42,"tunes":602},"Eti88tz1T6",{"text":601,"level":263},"Fraîcheur",{},{"id":604,"data":605,"type":224,"tunes":607},"3zKe198lls",{"text":606},"Quelle est la probabilité que l'information requise ait changé ? La capitale de la France a une très faible volatilité. Le cours d'une action a une volatilité extrêmement élevée.",{},{"id":609,"data":610,"type":42,"tunes":612},"ryQRR7TzC7",{"text":611,"level":263},"Spécificité",{},{"id":614,"data":615,"type":224,"tunes":617},"bkXBBuCBb_",{"text":616},"La question nécessite-t-elle des informations provenant d'une source, d'un document, d'une organisation, d'un compte ou d'un ensemble de données particulier ? Si l'utilisateur demande ce que dit un contrat spécifique, les connaissances générales du modèle sont non pertinentes. Le contrat doit être récupéré.",{},{"id":619,"data":620,"type":42,"tunes":622},"LlT6c-tPU2",{"text":621,"level":263},"Exigence de preuve",{},{"id":624,"data":625,"type":224,"tunes":627},"1G-aWjGT1c",{"text":626},"La réponse a-t-elle besoin d'une provenance ? Un modèle peut savoir qu'une affirmation est généralement acceptée mais avoir tout de même besoin d'une source lorsque la tâche exige une vérification.",{},{"id":629,"data":630,"type":42,"tunes":632},"lnoOCm4KDw",{"text":631,"level":263},"Couverture des connaissances",{},{"id":634,"data":635,"type":224,"tunes":637},"nKGrZO0Zw0",{"text":636},"Le sujet est-il susceptible d'être représenté adéquatement dans les connaissances internes du modèle ? Des informations rares, propriétaires, très locales ou nouvellement publiées créent une pression de récupération plus forte.",{},{"id":639,"data":640,"type":42,"tunes":642},"SYp_4G0qXz",{"text":641,"level":263},"Conséquence d'une erreur",{},{"id":644,"data":645,"type":224,"tunes":647},"YJeo8nKsl9",{"text":646},"Toutes les réponses incorrectes n'ont pas le même impact. Lorsque l'exactitude factuelle affecte matériellement une décision, le seuil de preuve acceptable peut être plus élevé.",{},{"id":649,"data":650,"type":224,"tunes":652},"NTh27HJjo1",{"text":651},"Ces variables n'ont pas besoin d'être implémentées sous forme de scores numériques littéraux. Elles décrivent la surface de décision.",{},{"id":654,"data":655,"type":42,"tunes":657},"A25id0cm1s",{"text":656,"level":218},"Méthode de diagnostic \u002F de décision",{},{"id":659,"data":660,"type":224,"tunes":662},"az70f7cIIF",{"text":661},"Un déclencheur de récupération très simple peut être implémenté sans apprentissage automatique.",{},{"id":664,"data":665,"type":295,"tunes":667},"yUFgVx9VRM",{"code":666},"def should_retrieve(\n    time_sensitive=False,\n    source_specific=False,\n    evidence_required=False,\n    private_context=False,\n    knowledge_uncertain=False,\n    conflicting_information=False\n):\n    return any([\n        time_sensitive,\n        source_specific,\n        evidence_required,\n        private_context,\n        knowledge_uncertain,\n        conflicting_information,\n    ])",{},{"id":669,"data":670,"type":224,"tunes":672},"tBn6sOGnKB",{"text":671},"Pour une question factuelle stable :",{},{"id":674,"data":675,"type":295,"tunes":677},"BW2rsTbqqL",{"code":676},"should_retrieve()\n# False",{},{"id":679,"data":680,"type":224,"tunes":682},"iriE0iq97f",{"text":681},"Pour un cours actuel :",{},{"id":684,"data":685,"type":295,"tunes":687},"d10aolm-TW",{"code":686},"should_retrieve(\n    time_sensitive=True\n)\n# True",{},{"id":689,"data":690,"type":224,"tunes":692},"nwL_vpUi-Y",{"text":691},"Pour une affirmation scientifique :",{},{"id":694,"data":695,"type":295,"tunes":697},"-DXgs4BBKH",{"code":696},"should_retrieve(\n    source_specific=True,\n    evidence_required=True\n)\n# True",{},{"id":699,"data":700,"type":224,"tunes":702},"Wj1sAbZ7l8",{"text":701},"Les systèmes de production peuvent rendre cette décision bien plus sophistiquée. Un classifieur pourrait prédire les besoins de récupération. Un modèle pourrait émettre des jetons de contrôle spéciaux. Un routeur pourrait classifier la complexité de la requête. La récupération pourrait également être déclenchée de manière répétée pendant la génération.",{},{"id":704,"data":705,"type":224,"tunes":707},"loLbe4TkAK",{"text":706},"L'implémentation peut changer. La question architecturale reste la même :",{},{"id":709,"data":710,"type":257,"tunes":713},"T2PJWaSYp9",{"text":711,"caption":712,"alignment":256},"Les preuves actuellement disponibles pour le modèle sont-elles suffisantes pour la réponse qu'il s'apprête à produire ?","",{},{"id":715,"data":716,"type":42,"tunes":718},"9Alw1zzG4E",{"text":717,"level":218},"Preuves",{},{"id":720,"data":721,"type":224,"tunes":723},"OgqdUwG1J-",{"text":722},"Le concept proposé ici est cohérent avec plusieurs axes de recherche sur la récupération.",{},{"id":725,"data":726,"type":224,"tunes":728},"QxIkEDnlBu",{"text":727},"L'\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">architecture RAG\u003C\u002Fa> originale a démontré l'utilité de combiner les connaissances paramétriques d'un modèle avec des connaissances externes non paramétriques, en particulier pour les tâches à forte intensité de connaissances.",{},{"id":730,"data":731,"type":224,"tunes":733},"nqdi_kDLE3",{"text":732},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> explore explicitement la récupération active pendant la génération, y compris la récupération déclenchée par un contenu à venir de faible confiance.",{},{"id":735,"data":736,"type":224,"tunes":738},"UThAFgyEe3",{"text":737},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa> démontre une architecture dans laquelle la récupération peut se produire à la demande et est suivie d'une réflexion sur les passages récupérés et le contenu généré.",{},{"id":740,"data":741,"type":224,"tunes":743},"CT8n5F5KLR",{"text":742},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> choisit dynamiquement parmi différentes stratégies selon la complexité de la question, y compris les situations où aucune récupération n'est nécessaire.",{},{"id":745,"data":746,"type":224,"tunes":748},"RDjo1UGf2s",{"text":747},"Le terme Retrieval Trigger est utilisé ici comme une abstraction au niveau du système pour cette famille plus large de décisions.",{},{"id":750,"data":751,"type":224,"tunes":753},"nmWZ-exi8b",{"text":752},"Il ne prétend pas que ces articles utilisent la même terminologie. Il identifie plutôt le problème architectural commun : qu'est-ce qui amène un système d'IA à passer de connaissances internes à des preuves externes ?",{},{"id":755,"data":756,"type":42,"tunes":758},"8a-H_OlfG1",{"text":757,"level":218},"Exemples réels",{},{"id":760,"data":761,"type":224,"tunes":763},"l0KONt5Buo",{"text":762},"Considérez un assistant de support connecté à la documentation d'une entreprise.",{},{"id":765,"data":766,"type":295,"tunes":768},"MOy11BOFq5",{"code":767},"\"How do I reset my password?\"",{},{"id":770,"data":771,"type":224,"tunes":773},"tsZcuMS1sT",{"text":772},"Si la procédure est stable et représentée de manière fiable dans les instructions actuelles de l'assistant, une réponse directe peut être appropriée.",{},{"id":775,"data":776,"type":295,"tunes":778},"l53NPB6WNV",{"code":777},"\"What permissions does my account currently have?\"",{},{"id":780,"data":781,"type":224,"tunes":783},"RKQXMbXA29",{"text":782},"Cette information est spécifique à l'utilisateur et dynamique. Le déclencheur de récupération se déclenche. Le système doit inspecter les données réelles du compte ou d'autorisation.",{},{"id":785,"data":786,"type":295,"tunes":788},"b5tNqxBKir",{"code":787},"\"Why was my production deployment rejected yesterday?\"",{},{"id":790,"data":791,"type":224,"tunes":793},"xaP7a7lV3i",{"text":792},"Le modèle peut comprendre les systèmes de déploiement et expliquer les raisons courantes. Mais la question porte sur un événement particulier. Les journaux, la sortie CI\u002FCD ou les enregistrements d'incidents sont nécessaires.",{},{"id":795,"data":796,"type":224,"tunes":798},"SlBdofaCVq",{"text":797},"La même logique s'applique à la recherche web.",{},{"id":800,"data":801,"type":295,"tunes":803},"VgFaQjUMnU",{"code":802},"\"What is RAG?\"",{},{"id":805,"data":806,"type":224,"tunes":808},"6BG7aSJQzt",{"text":807},"Une explication générale peut ne pas nécessiter de récupération.",{},{"id":810,"data":811,"type":295,"tunes":813},"TRngQB41uY",{"code":812},"\"What did the authors of Self-RAG specifically conclude about unnecessary retrieval?\"",{},{"id":815,"data":816,"type":224,"tunes":818},"7UIuDjIRyG",{"text":817},"Maintenant, des preuves spécifiques à la source sont requises.",{},{"id":820,"data":821,"type":295,"tunes":823},"CG2PbVS1yz",{"code":822},"\"What is the latest research on adaptive retrieval?\"",{},{"id":825,"data":826,"type":224,"tunes":828},"G1gyMnE_E8",{"text":827},"Cela introduit également une exigence de fraîcheur. Le sujet sous-jacent n'a pas changé. L'exigence d'information a changé.",{},{"id":830,"data":831,"type":42,"tunes":833},"NyJtHsPsSf",{"text":832,"level":218},"Idées fausses courantes et modes de défaillance",{},{"id":835,"data":836,"type":224,"tunes":838},"1otM6VenxR",{"text":837},"Plus de récupération produit automatiquement une meilleure réponse. Ce n'est pas le cas. Les documents non pertinents consomment du contexte et peuvent distraire la génération.",{},{"id":840,"data":841,"type":224,"tunes":843},"7BpMfX7lOZ",{"text":842},"Une confiance élevée du modèle signifie que la récupération est inutile. Un modèle peut produire une réponse incorrecte avec assurance. La confiance auto-déclarée ne doit donc pas être traitée comme le seul déclencheur.",{},{"id":845,"data":846,"type":224,"tunes":848},"THz75XkfrR",{"text":847},"Une récupération réussie signifie que la réponse est vérifiée. La récupération ne fournit que des preuves candidates. Les preuves doivent encore être pertinentes, suffisamment faisant autorité et correctement interprétées.",{},{"id":850,"data":851,"type":224,"tunes":853},"gOUGv2dAaq",{"text":852},"Le RAG résout automatiquement les connaissances obsolètes. Il ne le fait que si le corpus de récupération lui-même contient des informations à jour. Récupérer un document obsolète ne crée pas une réponse actuelle.",{},{"id":855,"data":856,"type":224,"tunes":858},"Mz8i-je--k",{"text":857},"Une étape de récupération suffit toujours. Les questions complexes peuvent nécessiter plusieurs éléments de preuve ou une récupération itérative.",{},{"id":860,"data":861,"type":42,"tunes":863},"imAEotM35y",{"text":862,"level":218},"Cas limites",{},{"id":865,"data":866,"type":224,"tunes":868},"8xkcG8hc9c",{"text":867},"Certaines questions contiennent à la fois des informations stables et instables.",{},{"id":870,"data":871,"type":295,"tunes":873},"IYDiRezoWn",{"code":872},"\"Who founded NVIDIA, and what is its market capitalization today?\"",{},{"id":875,"data":876,"type":224,"tunes":878},"2kxOM8vxYh",{"text":877},"La première partie peut être résolue à partir des connaissances stables du modèle. La seconde partie nécessite des informations actuelles.",{},{"id":880,"data":881,"type":224,"tunes":883},"6PyzlxURFS",{"text":882},"Un système suffisamment capable ne devrait pas nécessairement traiter l'ensemble de la requête comme une seule décision de récupération. Il peut déclencher la récupération uniquement là où c'est nécessaire.",{},{"id":885,"data":886,"type":224,"tunes":888},"lsbZ8aQAD6",{"text":887},"Un autre cas limite est le désaccord entre les sources. Supposons que la récupération renvoie trois documents contenant des affirmations incompatibles.",{},{"id":890,"data":891,"type":224,"tunes":893},"-Y67JvJusX",{"text":892},"Le Déclencheur de Récupération a déjà réussi : le système a reconnu que des preuves externes étaient nécessaires. Mais la tâche n'est pas terminée.",{},{"id":895,"data":896,"type":224,"tunes":898},"32VdDErqUM",{"text":897},"Le système est maintenant confronté à un problème d'évaluation des preuves. C'est là que la Frontière de Validité de la Réponse devient importante.",{},{"id":900,"data":901,"type":224,"tunes":903},"edCyD-PqlU",{"text":902},"Le système peut avoir récupéré des informations et ne pas encore posséder suffisamment de preuves pour tirer une conclusion solide.",{},{"id":905,"data":906,"type":295,"tunes":908},"rmjW0MBcFo",{"code":907},"Retrieval Trigger\n≠\npermission to answer",{},{"id":910,"data":911,"type":224,"tunes":913},"gZBq0voX0-",{"text":912},"Le déclencheur obtient des preuves. La frontière de validité détermine si ces preuves sont suffisantes.",{},{"id":915,"data":916,"type":42,"tunes":918},"DmO9cFY93l",{"text":917,"level":218},"Limites",{},{"id":920,"data":921,"type":224,"tunes":923},"gV4YT_2O1X",{"text":922},"Le Déclencheur de Récupération est un cadre conceptuel, pas un algorithme universel.",{},{"id":925,"data":926,"type":224,"tunes":928},"7c6OA2X4-H",{"text":927},"Différents systèmes nécessiteront différentes règles de déclenchement. Un bot de support client, un assistant de recherche scientifique, un moteur de recherche et un agent logiciel autonome n'ont pas des exigences de preuves identiques.",{},{"id":930,"data":931,"type":224,"tunes":933},"Xn8K4ArjdA",{"text":932},"Les seuils de déclenchement peuvent également créer leurs propres modes de défaillance. Un seuil trop bas provoque une récupération excessive. Un seuil trop élevé provoque des réponses non étayées.",{},{"id":935,"data":936,"type":224,"tunes":938},"y0gYRFZx6m",{"text":937},"L'infrastructure de récupération elle-même compte également. Un déclencheur parfait connecté à une mauvaise collection de sources produit toujours des preuves médiocres.",{},{"id":940,"data":941,"type":224,"tunes":943},"Kcvx1v4Z1x",{"text":942},"De même, une excellente base de connaissances offre peu de valeur si le déclencheur ne s'active jamais lorsqu'il est nécessaire.",{},{"id":945,"data":946,"type":224,"tunes":948},"wcRpKvBhkb",{"text":947},"Le Déclencheur de Récupération ne résout donc qu'une partie d'une architecture plus vaste.",{},{"id":950,"data":951,"type":42,"tunes":953},"YN1_g7vs7V",{"text":952,"level":218},"Qu'est-ce qui changerait cette réponse ?",{},{"id":955,"data":956,"type":224,"tunes":958},"4PYX_PYK_Z",{"text":957},"Les futurs modèles pourraient contenir de meilleurs mécanismes pour identifier leurs propres limites de connaissances. Les récupérateurs pourraient devenir moins chers et plus rapides. Les systèmes à long contexte pourraient transporter beaucoup plus de matériel source en continu.",{},{"id":960,"data":961,"type":224,"tunes":963},"vyqJ8Kp8Ye",{"text":962},"Les modèles peuvent aussi de plus en plus combiner recherche, bases de données, outils et connaissances structurées sans exposer une étape RAG distincte au développeur d'application.",{},{"id":965,"data":966,"type":224,"tunes":968},"_KN0MPs6as",{"text":967},"Ces changements pourraient modifier la façon dont le déclencheur est implémenté. Ils ne suppriment pas nécessairement la décision sous-jacente.",{},{"id":970,"data":971,"type":224,"tunes":973},"Zrlr4a0utJ",{"text":972},"Tant qu'il existe une différence entre les informations déjà disponibles pour le modèle et les informations qui doivent être obtenues à l'extérieur, un système a encore besoin d'un mécanisme pour déterminer quand franchir cette frontière.",{},{"id":975,"data":976,"type":224,"tunes":978},"4hl7r5cF1M",{"text":977},"L'implémentation peut disparaître de la vue. La question architecturale demeure.",{},{"id":980,"data":981,"type":42,"tunes":983},"o_g5g_6eSj",{"text":982,"level":218},"Conclusion",{},{"id":985,"data":986,"type":224,"tunes":988},"L6MWm8xdAg",{"text":987},"Le RAG commence trop tard pour expliquer tout le problème.",{},{"id":990,"data":991,"type":224,"tunes":993},"uymgoYlFM5",{"text":992},"Avant que la récupération puisse avoir lieu, un système d'IA doit déterminer si la récupération est nécessaire. Cette décision est le Déclencheur de Récupération.",{},{"id":995,"data":996,"type":295,"tunes":998},"_KIblg0ae_",{"code":997},"Stable known fact\n→ answer from model knowledge\n\nCurrent fact\n→ retrieve\n\nSource-specific or evidence-dependent claim\n→ retrieve and verify",{},{"id":1000,"data":1001,"type":224,"tunes":1003},"unCgfbeYI5",{"text":1002},"Mais l'implication plus large est plus importante. Une IA fiable n'a pas seulement besoin d'accéder à la connaissance. Elle a besoin d'une méthode pour déterminer quand sa connaissance actuelle est insuffisante.",{},{"id":1005,"data":1006,"type":295,"tunes":1008},"ZAosU4trn9",{"code":1007},"Model Knowledge\n        ↓\nRetrieval Trigger\n        ↓\nRuntime Knowledge \u002F RAG\n        ↓\nEvidence\n        ↓\nReasoning\n        ↓\nAnswer Validity Boundary\n        ↓\nAnswer",{},{"id":1010,"data":1011,"type":224,"tunes":1013},"f0ZIysaJy1",{"text":1012},"Le Déclencheur de Récupération détermine quand le système doit chercher des preuves. La Frontière de Validité de la Réponse détermine si ces preuves sont suffisantes.",{},{"id":1015,"data":1016,"type":224,"tunes":1018},"iCg9ojv75m",{"text":1017},"Ensemble, ils décrivent quelque chose de plus utile que le RAG seul : un processus de décision pour passer de ce qu'une IA semble savoir à ce qu'elle peut réellement soutenir.",{},{"id":1020,"data":1021,"type":42,"tunes":1023},"cDBiNnZJv-",{"text":1022,"level":218},"Sources Primaires",{},{"id":1025,"data":1026,"type":224,"tunes":1028},"8gumvODB16",{"text":1027},"Patrick Lewis et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\u003C\u002Fa> (2020). Travail fondateur sur le RAG décrivant la combinaison de la mémoire paramétrique du modèle avec une mémoire externe non paramétrique.",{},{"id":1030,"data":1031,"type":224,"tunes":1033},"Chz6I7zmlv",{"text":1032},"Zhengbao Jiang et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">Active Retrieval Augmented Generation\u003C\u002Fa> (2023). Introduit FLARE et la récupération active pendant la génération, y compris la récupération basée sur un contenu prédit à faible confiance.",{},{"id":1035,"data":1036,"type":224,"tunes":1038},"MRdjivpsoW",{"text":1037},"Akari Asai et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection\u003C\u002Fa> (2023). Explore la récupération adaptative à la demande et l'auto-réflexion au lieu d'une récupération fixe inconditionnelle.",{},{"id":1040,"data":1041,"type":224,"tunes":1043},"lck29euXJP",{"text":1042},"Soyeong Jeong et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity\u003C\u002Fa> (2024). Sélectionne dynamiquement entre aucune récupération, une récupération en une étape et des stratégies de récupération plus complexes selon la question entrante.",{},"2.31","Un modèle d'IA n'a pas besoin de récupération pour chaque question. Le problème important est de savoir quand ses connaissances internes ne suffisent plus. Le Déclencheur de Récupération est une frontière de décision pratique qui détermine quand un système d'IA devrait cesser de se fier uniquement aux connaissances du modèle et obtenir des preuves externes avant de répondre.","\u002Fuploads\u002F2026\u002F09\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger-1790574991244-f4rpyg.webp","when-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger-1790574991244-f4rpyg","PUBLISHED","2026-09-28T01:49:00.000Z","2026-09-28T05:49:59.593Z","2026-09-28T06:02:54.212Z",{"en":1053,"de":1054,"sr":1055,"es":1056,"fr":1057,"it":1058,"ru":1059,"zh":1060},"\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fde\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fsr\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fes\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Ffr\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fit\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fru\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fzh\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger",[1062,1065,1069,1073,1077,1081],{"id":101,"name":1063,"slug":1064},"Overview","overview-digital-platform",{"id":1066,"name":1067,"slug":1068},57,"Limites des données","data-boundaries",{"id":1070,"name":1071,"slug":1072},51,"Anti-patterns","anti-patterns",{"id":1074,"name":1075,"slug":1076},58,"Évaluation et garde-fous qualité","evaluation",{"id":1078,"name":1079,"slug":1080},56,"Portefeuille de cas d’usage","use-case-portfolio",{"id":1082,"name":1083,"slug":1084},60,"Contrôles de coût et latence","cost-and-latency",{"id":1086,"login":1087,"email":1088,"displayName":1089},"20","rooth8233","aleksandar@stajic.de","Aleksandar Stajić",[1091,1736],{"lang":1092,"title":1093,"content":1094,"contentJson":1095,"excerpt":1735},"en","When Should an AI Stop Trusting Its Own Knowledge? — The Retrieval Trigger","{\"time\":1790574879391,\"blocks\":[{\"id\":\"Wt7UfNeFlS\",\"data\":{\"text\":\"Question\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"T-ZCQblBzm\",\"data\":{\"text\":\"When should an AI stop relying on what it already knows and retrieve external information before answering?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"vBcd4061WS\",\"data\":{\"text\":\"This question appears simple, but it sits at the center of one of the most important design decisions in modern AI systems.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"r9NZ-Fzw0e\",\"data\":{\"text\":\"Large language models contain substantial knowledge in their parameters. Retrieval-Augmented Generation adds external information at runtime. But neither extreme is ideal.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"CpzlgJjAVL\",\"data\":{\"text\":\"Always trusting the model can produce outdated or unsupported answers. Always retrieving information adds latency, cost, irrelevant context and new opportunities for retrieval errors.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"yeclJhYJ1a\",\"data\":{\"text\":\"The real problem therefore comes before RAG: When should retrieval happen at all?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"FgLSWvpZMg\",\"data\":{\"text\":\"This article uses the term Retrieval Trigger for that decision. Retrieval Trigger is not presented here as a standardized term from the research literature. It is a practical systems concept that brings together ideas already visible in research on active, adaptive and self-reflective retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Muzvv-2uzU\",\"data\":{\"text\":\"A Retrieval Trigger is a condition indicating that an AI system should stop relying solely on internal model knowledge and obtain external evidence before producing or finalizing an answer.\",\"caption\":\"Working definition\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"1BGt1waZ01\",\"data\":{\"title\":\"Contents\",\"maxLevel\":3,\"minLevel\":2},\"type\":\"tableOfContents\",\"tunes\":{}},{\"id\":\"BFKJ2htjYN\",\"data\":{\"text\":\"What This Really Means\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"yfBYqVwObv\",\"data\":{\"text\":\"An LLM has two fundamentally different ways of obtaining information.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Y4JYebztDi\",\"data\":{\"text\":\"The first is model knowledge. This is information represented in the model's learned parameters. No database query, web search or document lookup is required at runtime.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"x2L37FSTBK\",\"data\":{\"text\":\"The second is runtime knowledge. This is information provided while the model is operating: search results, database records, documents, APIs, user files, tool outputs or other retrieved evidence.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"2szDUb7_-4\",\"data\":{\"text\":\"RAG connects these two worlds. But RAG itself does not answer the question of when that connection should be activated. That is the purpose of the Retrieval Trigger.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"5_yjTthHV4\",\"data\":{\"code\":\"Question\\n   ↓\\nModel Knowledge\\n   ↓\\nIs internal knowledge sufficient?\\n   ↓\\nRetrieval Trigger\\n   ↓\\nExternal Retrieval, if required\\n   ↓\\nEvidence\\n   ↓\\nReasoning\\n   ↓\\nAnswer Validity Boundary\\n   ↓\\nAnswer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"rH2K36ambR\",\"data\":{\"text\":\"The Retrieval Trigger therefore sits before retrieval. The Answer Validity Boundary sits later.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"L0WlGs_dTF\",\"data\":{\"text\":\"The first asks: Do I need external evidence?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"JyE4O9aDCW\",\"data\":{\"text\":\"The second asks: Do I now have enough evidence to support this answer?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"L9JP5xByy4\",\"data\":{\"text\":\"These are related decisions, but they are not the same decision.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"4hPbiDSHek\",\"data\":{\"text\":\"Simplest Example\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"cER32Me6gA\",\"data\":{\"text\":\"Consider three questions.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"izi7nU9FE9\",\"data\":{\"content\":[[\"Question\",\"Internal knowledge\",\"Retrieval Trigger\"],[\"What is the capital of France?\",\"Usually sufficient\",\"No strong trigger\"],[\"What is the current NVIDIA stock price?\",\"Potentially outdated\",\"Trigger retrieval\"],[\"Does this new scientific paper prove that X causes Y?\",\"Cannot establish the claim without examining the evidence\",\"Strong retrieval trigger\"]],\"stretched\":false,\"withHeadings\":true},\"type\":\"table\",\"tunes\":{}},{\"id\":\"cb-Kx0fKs4\",\"data\":{\"text\":\"The first question is based on a highly stable fact.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"MZJzwvZUH7\",\"data\":{\"code\":\"User\\n↓\\n\\\"What is the capital of France?\\\"\\n\\nModel knowledge\\n↓\\nParis\\n\\nFresh external evidence required?\\n↓\\nNo\\n\\nAnswer\\n↓\\nParis\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"fRP7-aWTJB\",\"data\":{\"text\":\"Retrieving documents before answering would usually add little value.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"O2TaSvLoxO\",\"data\":{\"text\":\"Now consider a question whose answer changes continuously.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"cNv0Dp7Mk3\",\"data\":{\"code\":\"User\\n↓\\n\\\"What is the current NVIDIA stock price?\\\"\\n\\nModel knowledge\\n↓\\nPotentially outdated\\n\\nCurrent information required?\\n↓\\nYes\\n\\nRETRIEVAL TRIGGER\\n↓\\nMarket data \u002F search \u002F API\\n↓\\nAnswer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"Y3NDw8awnA\",\"data\":{\"text\":\"The model may know a great deal about NVIDIA. That does not mean it knows the price now.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"FwjiaA6mdJ\",\"data\":{\"text\":\"The third example is even more important.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"G48ZGtX4XK\",\"data\":{\"code\":\"User\\n↓\\n\\\"Does this new scientific paper prove that X causes Y?\\\"\\n\\nModel knowledge\\n↓\\nCan reason about causality,\\nstatistics and scientific methodology.\\n\\nBut:\\nthe actual evidence is not available internally.\\n\\nRETRIEVAL TRIGGER\\n↓\\nRetrieve the paper\\n↓\\nInspect methodology\\n↓\\nInspect results\\n↓\\nCompare claim with evidence\\n↓\\nAnswer Validity Boundary\\n↓\\nAnswer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"nGu-KcQC6l\",\"data\":{\"text\":\"The model's reasoning capability may be perfectly useful. The missing component is evidence.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"_bUxnOYvHG\",\"data\":{\"text\":\"That distinction is fundamental.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"etbE_esRx4\",\"data\":{\"text\":\"Where the Example Stops Working\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"0iSdy2Msw7\",\"data\":{\"text\":\"The examples above make the decision appear binary: retrieve or do not retrieve.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"8Go7nm2niJ\",\"data\":{\"text\":\"Real systems are more complicated. A question may contain several claims, some stable and some current. Retrieved documents may disagree. A retriever may return irrelevant information. The relevant information may exist but fail to rank highly enough. A document may be authoritative but outdated.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"8dVjRU5cXg\",\"data\":{\"text\":\"Retrieval itself can also introduce incorrect context into an otherwise reasonable answer.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"pLqSH5-OJR\",\"data\":{\"text\":\"This is why retrieval should not be treated as an automatic synonym for truth.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"ww4Od2cmTr\",\"data\":{\"text\":\"Research on adaptive retrieval has increasingly moved away from the assumption that every query should receive the same retrieval strategy.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"1yE2LUP7cF\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\\\" target=\\\"_blank\\\">Self-RAG\u003C\u002Fa>, for example, explicitly explores retrieval on demand rather than indiscriminately retrieving a fixed number of passages for every input. The authors discuss how unnecessary or irrelevant retrieval can reduce answer quality.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"915QBDW89m\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\\\" target=\\\"_blank\\\">Adaptive-RAG\u003C\u002Fa> similarly selects between no retrieval, single-step retrieval and more complex retrieval strategies according to question complexity.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"1FBgxY0QQp\",\"data\":{\"text\":\"So the important question is not: Does this system have RAG?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"4xdj86u8Qz\",\"data\":{\"text\":\"It is: Can this system recognize when retrieval is necessary and what kind of retrieval is appropriate?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"bGPa0AsJI6\",\"data\":{\"text\":\"Direct Answer\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"fBKcyJ0IcX\",\"data\":{\"text\":\"An AI should trigger retrieval when answering requires information that its internal model knowledge cannot safely provide with the required freshness, specificity, provenance or evidential support.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"JbIPIJjkXK\",\"data\":{\"text\":\"In practical systems, a Retrieval Trigger can emerge from several conditions:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"_JbTSHlrtH\",\"data\":{\"code\":\"Need for current information\\n        OR\\nNeed for exact source-specific information\\n        OR\\nNeed for evidence or provenance\\n        OR\\nNeed for private\u002Fuser-specific information\\n        OR\\nInsufficient knowledge coverage\\n        OR\\nConflicting evidence\\n        OR\\nHigh consequence of factual error\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"Aaem6fQ_tF\",\"data\":{\"text\":\"If none of these conditions is materially present, retrieval may be unnecessary. If one or more are present, external evidence becomes part of the answer-generation process.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"x2DDg7Ue1-\",\"data\":{\"text\":\"Why This Is So\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"1GTaWG9ViB\",\"data\":{\"text\":\"A language model's internal knowledge is often described as parametric knowledge. It was learned during training and encoded into the model's parameters.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"klwNY3lr1d\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\\\" target=\\\"_blank\\\">Lewis et al.'s original RAG work\u003C\u002Fa> framed retrieval as a combination of this parametric memory with external, non-parametric memory. The external memory can be searched and updated without retraining the entire language model.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Fyw2AVDbxR\",\"data\":{\"text\":\"This distinction creates an unavoidable systems problem.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"4FvbthV3in\",\"data\":{\"text\":\"The model can know things. But the model cannot assume that everything it knows is current, complete, specific enough and supported by the required evidence.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"asxdihTbcB\",\"data\":{\"text\":\"A model can therefore produce a linguistically convincing answer while still operating beyond the point where its internal knowledge is sufficient.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"jGgq116uAa\",\"data\":{\"text\":\"That point is where a Retrieval Trigger becomes useful.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"T6q_BUeDg3\",\"data\":{\"text\":\"Context\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"9H_bNlyoYs\",\"data\":{\"text\":\"Traditional RAG often looks like this:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"YSZR1AInSj\",\"data\":{\"code\":\"Question\\n↓\\nRetrieve documents\\n↓\\nAdd documents to context\\n↓\\nGenerate answer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"HnzY2Q9xTs\",\"data\":{\"text\":\"This architecture assumes retrieval before generation. That works well for many knowledge-intensive applications, but it can also perform unnecessary retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Aho03YTGAU\",\"data\":{\"text\":\"More advanced approaches introduce an adaptive step:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"uK0l0tYLg6\",\"data\":{\"code\":\"Question\\n↓\\nEvaluate information requirement\\n↓\\n        ┌───────────────┐\\n        │               │\\n   no retrieval      retrieval\\n        │               │\\n        ↓               ↓\\n model knowledge    external evidence\\n        │               │\\n        └───────┬───────┘\\n                ↓\\n              answer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"Upb-15aN8T\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\\\" target=\\\"_blank\\\">FLARE\u003C\u002Fa> goes further by considering retrieval during generation itself. It uses upcoming generation and low-confidence tokens as signals for retrieving additional information.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"I5hs5j9IKc\",\"data\":{\"text\":\"Self-RAG similarly introduces mechanisms allowing retrieval, generation and critique to interact instead of treating retrieval as an unconditional preprocessing step.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"mw2jbuWA-g\",\"data\":{\"text\":\"Adaptive-RAG approaches the same broader problem from query complexity: different questions may require different retrieval strategies.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"DUba0EfbWg\",\"data\":{\"text\":\"These approaches differ technically. But they expose the same architectural insight: Retrieval should be a decision, not merely a permanent switch.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"wzX0jC8H8b\",\"data\":{\"text\":\"Assumptions\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"4puAk8h-NF\",\"data\":{\"text\":\"The Retrieval Trigger framework assumes that a system has access to at least one external information source when retrieval is required.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Qsm42lc7aC\",\"data\":{\"text\":\"That source could be web search, a document store, vector database, SQL database, knowledge graph, API, enterprise system, user-uploaded document or tool output.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Rznt7yvqT2\",\"data\":{\"text\":\"It also assumes that retrieval has a cost. That cost does not have to be financial.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"wQoEfZuFPe\",\"data\":{\"text\":\"Retrieval introduces latency, token consumption, context usage, infrastructure complexity and the possibility of retrieving misleading information.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"WM1F9QkT2G\",\"data\":{\"text\":\"The optimal system therefore does not maximize retrieval. It maximizes appropriate retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Z4gw9SX7jo\",\"data\":{\"text\":\"Variables\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"Z_sKNO6vmp\",\"data\":{\"text\":\"A practical Retrieval Trigger can consider five primary variables.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Eti88tz1T6\",\"data\":{\"text\":\"Freshness\",\"level\":3},\"type\":\"header\",\"tunes\":{}},{\"id\":\"3zKe198lls\",\"data\":{\"text\":\"How likely is the required information to have changed? The capital of France has very low volatility. A stock price has extremely high volatility.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"ryQRR7TzC7\",\"data\":{\"text\":\"Specificity\",\"level\":3},\"type\":\"header\",\"tunes\":{}},{\"id\":\"bkXBBuCBb_\",\"data\":{\"text\":\"Does the question require information from a particular source, document, organization, account or dataset? If the user asks what a specific contract says, general model knowledge is irrelevant. The contract must be retrieved.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"LlT6c-tPU2\",\"data\":{\"text\":\"Evidence Requirement\",\"level\":3},\"type\":\"header\",\"tunes\":{}},{\"id\":\"1G-aWjGT1c\",\"data\":{\"text\":\"Does the answer need provenance? A model may know that a claim is generally accepted but still need a source when the task requires verification.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"lnoOCm4KDw\",\"data\":{\"text\":\"Knowledge Coverage\",\"level\":3},\"type\":\"header\",\"tunes\":{}},{\"id\":\"nKGrZO0Zw0\",\"data\":{\"text\":\"Is the subject likely to be represented adequately in internal model knowledge? Rare, proprietary, highly local or newly published information creates stronger retrieval pressure.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"SYp_4G0qXz\",\"data\":{\"text\":\"Consequence of Error\",\"level\":3},\"type\":\"header\",\"tunes\":{}},{\"id\":\"YJeo8nKsl9\",\"data\":{\"text\":\"Not every incorrect answer has the same impact. Where factual accuracy materially affects a decision, the acceptable evidence threshold may be higher.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"NTh27HJjo1\",\"data\":{\"text\":\"These variables do not have to be implemented as literal numeric scores. They describe the decision surface.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"A25id0cm1s\",\"data\":{\"text\":\"Diagnostic \u002F Decision Method\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"az70f7cIIF\",\"data\":{\"text\":\"A very simple Retrieval Trigger can be implemented without machine learning.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"yUFgVx9VRM\",\"data\":{\"code\":\"def should_retrieve(\\n    time_sensitive=False,\\n    source_specific=False,\\n    evidence_required=False,\\n    private_context=False,\\n    knowledge_uncertain=False,\\n    conflicting_information=False\\n):\\n    return any([\\n        time_sensitive,\\n        source_specific,\\n        evidence_required,\\n        private_context,\\n        knowledge_uncertain,\\n        conflicting_information,\\n    ])\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"tBn6sOGnKB\",\"data\":{\"text\":\"For a stable factual question:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"BW2rsTbqqL\",\"data\":{\"code\":\"should_retrieve()\\n# False\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"iriE0iq97f\",\"data\":{\"text\":\"For a current stock price:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"d10aolm-TW\",\"data\":{\"code\":\"should_retrieve(\\n    time_sensitive=True\\n)\\n# True\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"nwL_vpUi-Y\",\"data\":{\"text\":\"For a scientific claim:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"-DXgs4BBKH\",\"data\":{\"code\":\"should_retrieve(\\n    source_specific=True,\\n    evidence_required=True\\n)\\n# True\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"Wj1sAbZ7l8\",\"data\":{\"text\":\"Production systems can make this decision far more sophisticated. A classifier could predict retrieval requirements. A model could emit special control tokens. A router could classify query complexity. Retrieval could also be triggered repeatedly during generation.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"loLbe4TkAK\",\"data\":{\"text\":\"The implementation can change. The architectural question remains the same:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"T2PJWaSYp9\",\"data\":{\"text\":\"Is the evidence currently available to the model sufficient for the answer it is about to produce?\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"9Alw1zzG4E\",\"data\":{\"text\":\"Evidence\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"OgqdUwG1J-\",\"data\":{\"text\":\"The concept proposed here is consistent with several lines of retrieval research.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"QxIkEDnlBu\",\"data\":{\"text\":\"The original \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\\\" target=\\\"_blank\\\">RAG architecture\u003C\u002Fa> demonstrated the usefulness of combining parametric model knowledge with external non-parametric knowledge, particularly for knowledge-intensive tasks.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"nqdi_kDLE3\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\\\" target=\\\"_blank\\\">FLARE\u003C\u002Fa> explicitly explores active retrieval during generation, including retrieval prompted by low-confidence upcoming content.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"UThAFgyEe3\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\\\" target=\\\"_blank\\\">Self-RAG\u003C\u002Fa> demonstrates an architecture in which retrieval can occur on demand and is followed by reflection on retrieved passages and generated content.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"CT8n5F5KLR\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\\\" target=\\\"_blank\\\">Adaptive-RAG\u003C\u002Fa> dynamically chooses among different strategies according to question complexity, including situations where no retrieval is required.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"RDjo1UGf2s\",\"data\":{\"text\":\"The term Retrieval Trigger is used here as a system-level abstraction over this broader family of decisions.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"nmWZ-exi8b\",\"data\":{\"text\":\"It does not claim that these papers use the same terminology. Instead, it identifies the shared architectural problem: What causes an AI system to transition from internal knowledge to external evidence?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"8a-H_OlfG1\",\"data\":{\"text\":\"Real Examples\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"l0KONt5Buo\",\"data\":{\"text\":\"Consider a support assistant connected to a company's documentation.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"MOy11BOFq5\",\"data\":{\"code\":\"\\\"How do I reset my password?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"tsZcuMS1sT\",\"data\":{\"text\":\"If the procedure is stable and reliably represented in the assistant's current instructions, direct answering may be appropriate.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"l53NPB6WNV\",\"data\":{\"code\":\"\\\"What permissions does my account currently have?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"RKQXMbXA29\",\"data\":{\"text\":\"That information is user-specific and dynamic. The Retrieval Trigger fires. The system must inspect the actual account or authorization data.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"b5tNqxBKir\",\"data\":{\"code\":\"\\\"Why was my production deployment rejected yesterday?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"xaP7a7lV3i\",\"data\":{\"text\":\"The model can understand deployment systems and explain common reasons. But the question is asking about a particular event. Logs, CI\u002FCD output or incident records are required.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"SlBdofaCVq\",\"data\":{\"text\":\"The same logic works for web search.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"VgFaQjUMnU\",\"data\":{\"code\":\"\\\"What is RAG?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"6BG7aSJQzt\",\"data\":{\"text\":\"A general explanation may not require retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"TRngQB41uY\",\"data\":{\"code\":\"\\\"What did the authors of Self-RAG specifically conclude about unnecessary retrieval?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"7UIuDjIRyG\",\"data\":{\"text\":\"Now source-specific evidence is required.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"CG2PbVS1yz\",\"data\":{\"code\":\"\\\"What is the latest research on adaptive retrieval?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"G1gyMnE_E8\",\"data\":{\"text\":\"This introduces a freshness requirement as well. The underlying subject has not changed. The information requirement has.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"NyJtHsPsSf\",\"data\":{\"text\":\"Common Misconceptions and Failure Modes\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"1otM6VenxR\",\"data\":{\"text\":\"More retrieval automatically produces a better answer. It does not. Irrelevant documents consume context and can distract generation.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"7BpMfX7lOZ\",\"data\":{\"text\":\"High model confidence means retrieval is unnecessary. A model can produce an incorrect answer confidently. Self-reported confidence should therefore not be treated as the only trigger.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"THz75XkfrR\",\"data\":{\"text\":\"Successful retrieval means the answer is verified. Retrieval only provides candidate evidence. The evidence must still be relevant, sufficiently authoritative and correctly interpreted.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"gOUGv2dAaq\",\"data\":{\"text\":\"RAG automatically solves outdated knowledge. It only does so if the retrieval corpus itself contains current information. Retrieving an outdated document does not create a current answer.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Mz8i-je--k\",\"data\":{\"text\":\"One retrieval step is always enough. Complex questions may require several pieces of evidence or iterative retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"imAEotM35y\",\"data\":{\"text\":\"Edge Cases\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"8xkcG8hc9c\",\"data\":{\"text\":\"Some questions contain both stable and unstable information.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"IYDiRezoWn\",\"data\":{\"code\":\"\\\"Who founded NVIDIA, and what is its market capitalization today?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"2kxOM8vxYh\",\"data\":{\"text\":\"The first part may be answerable from stable model knowledge. The second part requires current information.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"6PyzlxURFS\",\"data\":{\"text\":\"A sufficiently capable system should not necessarily treat the entire query as one retrieval decision. It can trigger retrieval only where required.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"lsbZ8aQAD6\",\"data\":{\"text\":\"Another edge case is disagreement between sources. Suppose retrieval returns three documents making incompatible claims.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"-Y67JvJusX\",\"data\":{\"text\":\"The Retrieval Trigger has already succeeded: the system recognized that external evidence was required. But the task is not finished.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"32VdDErqUM\",\"data\":{\"text\":\"The system has now reached an evidence evaluation problem. This is where the Answer Validity Boundary becomes important.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"edCyD-PqlU\",\"data\":{\"text\":\"The system may have retrieved information and still not possess enough evidence to make a strong conclusion.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"rmjW0MBcFo\",\"data\":{\"code\":\"Retrieval Trigger\\n≠\\npermission to answer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"gZBq0voX0-\",\"data\":{\"text\":\"The trigger obtains evidence. The validity boundary determines whether that evidence is sufficient.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"DmO9cFY93l\",\"data\":{\"text\":\"Limitations\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"gV4YT_2O1X\",\"data\":{\"text\":\"The Retrieval Trigger is a conceptual framework, not a universal algorithm.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"7c6OA2X4-H\",\"data\":{\"text\":\"Different systems will require different trigger rules. A customer-support bot, scientific research assistant, search engine and autonomous software agent do not have identical evidence requirements.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Xn8K4ArjdA\",\"data\":{\"text\":\"Trigger thresholds can also create their own failure modes. A threshold that is too low causes excessive retrieval. A threshold that is too high causes unsupported answering.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"y0gYRFZx6m\",\"data\":{\"text\":\"The retrieval infrastructure itself also matters. A perfect trigger connected to a poor source collection still produces poor evidence.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Kcvx1v4Z1x\",\"data\":{\"text\":\"Similarly, an excellent knowledge base provides little value if the trigger never activates when it is needed.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"wcRpKvBhkb\",\"data\":{\"text\":\"The Retrieval Trigger therefore solves only one part of a larger architecture.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"YN1_g7vs7V\",\"data\":{\"text\":\"What Would Change This Answer?\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"4PYX_PYK_Z\",\"data\":{\"text\":\"Future models may contain better mechanisms for identifying their own knowledge limitations. Retrievers may become cheaper and faster. Long-context systems may carry far more source material continuously.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"vyqJ8Kp8Ye\",\"data\":{\"text\":\"Models may also increasingly combine search, databases, tools and structured knowledge without exposing a distinct RAG stage to the application developer.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"_KN0MPs6as\",\"data\":{\"text\":\"These changes could alter how the trigger is implemented. They do not necessarily remove the underlying decision.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Zrlr4a0utJ\",\"data\":{\"text\":\"As long as there is a difference between information already available to the model and information that must be obtained externally, a system still needs some mechanism for determining when to cross that boundary.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"4hl7r5cF1M\",\"data\":{\"text\":\"The implementation may disappear from view. The architectural question remains.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"o_g5g_6eSj\",\"data\":{\"text\":\"Conclusion\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"L6MWm8xdAg\",\"data\":{\"text\":\"RAG begins too late to explain the whole problem.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"uymgoYlFM5\",\"data\":{\"text\":\"Before retrieval can happen, an AI system must determine whether retrieval is necessary. That decision is the Retrieval Trigger.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"_KIblg0ae_\",\"data\":{\"code\":\"Stable known fact\\n→ answer from model knowledge\\n\\nCurrent fact\\n→ retrieve\\n\\nSource-specific or evidence-dependent claim\\n→ retrieve and verify\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"unCgfbeYI5\",\"data\":{\"text\":\"But the broader implication is more important. Reliable AI does not merely need access to knowledge. It needs a method for determining when its current knowledge is insufficient.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"ZAosU4trn9\",\"data\":{\"code\":\"Model Knowledge\\n        ↓\\nRetrieval Trigger\\n        ↓\\nRuntime Knowledge \u002F RAG\\n        ↓\\nEvidence\\n        ↓\\nReasoning\\n        ↓\\nAnswer Validity Boundary\\n        ↓\\nAnswer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"f0ZIysaJy1\",\"data\":{\"text\":\"The Retrieval Trigger determines when the system should seek evidence. The Answer Validity Boundary determines whether that evidence is sufficient.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"iCg9ojv75m\",\"data\":{\"text\":\"Together they describe something more useful than RAG alone: a decision process for moving from what an AI appears to know toward what it can actually support.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"cDBiNnZJv-\",\"data\":{\"text\":\"Primary Sources\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"8gumvODB16\",\"data\":{\"text\":\"Patrick Lewis et al., \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\\\" target=\\\"_blank\\\">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\u003C\u002Fa> (2020). Foundational RAG work describing the combination of parametric model memory with external non-parametric memory.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Chz6I7zmlv\",\"data\":{\"text\":\"Zhengbao Jiang et al., \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\\\" target=\\\"_blank\\\">Active Retrieval Augmented Generation\u003C\u002Fa> (2023). Introduces FLARE and active retrieval during generation, including retrieval based on low-confidence predicted content.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"MRdjivpsoW\",\"data\":{\"text\":\"Akari Asai et al., \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\\\" target=\\\"_blank\\\">Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection\u003C\u002Fa> (2023). Explores adaptive retrieval on demand and self-reflection instead of unconditional fixed retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"lck29euXJP\",\"data\":{\"text\":\"Soyeong Jeong et al., \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\\\" target=\\\"_blank\\\">Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity\u003C\u002Fa> (2024). Dynamically selects among no retrieval, single-step retrieval and more complex retrieval strategies according to the incoming question.\"},\"type\":\"paragraph\",\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1096,"blocks":1097,"version":1734},1790574879391,[1098,1101,1105,1109,1113,1117,1121,1125,1130,1134,1138,1142,1146,1150,1154,1157,1161,1165,1169,1173,1177,1181,1200,1204,1207,1211,1215,1218,1222,1226,1229,1233,1237,1241,1245,1249,1253,1257,1261,1265,1269,1273,1277,1281,1285,1289,1292,1296,1300,1304,1308,1312,1316,1320,1324,1328,1332,1335,1339,1343,1346,1350,1354,1358,1362,1366,1370,1374,1378,1382,1386,1389,1393,1397,1401,1405,1409,1413,1417,1421,1425,1429,1433,1437,1441,1445,1448,1452,1455,1459,1462,1466,1469,1473,1477,1481,1485,1489,1493,1497,1501,1505,1509,1513,1517,1521,1524,1528,1531,1535,1538,1542,1546,1549,1553,1556,1560,1563,1567,1571,1575,1579,1583,1587,1591,1595,1599,1602,1606,1610,1614,1618,1622,1626,1629,1633,1637,1641,1645,1649,1653,1657,1661,1665,1669,1673,1677,1681,1685,1688,1692,1696,1699,1703,1706,1710,1714,1718,1722,1726,1730],{"id":215,"data":1099,"type":42,"tunes":1100},{"text":217,"level":218},{},{"id":221,"data":1102,"type":224,"tunes":1104},{"text":1103},"When should an AI stop relying on what it already knows and retrieve external information before answering?",{},{"id":227,"data":1106,"type":224,"tunes":1108},{"text":1107},"This question appears simple, but it sits at the center of one of the most important design decisions in modern AI systems.",{},{"id":232,"data":1110,"type":224,"tunes":1112},{"text":1111},"Large language models contain substantial knowledge in their parameters. Retrieval-Augmented Generation adds external information at runtime. But neither extreme is ideal.",{},{"id":237,"data":1114,"type":224,"tunes":1116},{"text":1115},"Always trusting the model can produce outdated or unsupported answers. Always retrieving information adds latency, cost, irrelevant context and new opportunities for retrieval errors.",{},{"id":242,"data":1118,"type":224,"tunes":1120},{"text":1119},"The real problem therefore comes before RAG: When should retrieval happen at all?",{},{"id":247,"data":1122,"type":224,"tunes":1124},{"text":1123},"This article uses the term Retrieval Trigger for that decision. Retrieval Trigger is not presented here as a standardized term from the research literature. It is a practical systems concept that brings together ideas already visible in research on active, adaptive and self-reflective retrieval.",{},{"id":252,"data":1126,"type":257,"tunes":1129},{"text":1127,"caption":1128,"alignment":256},"A Retrieval Trigger is a condition indicating that an AI system should stop relying solely on internal model knowledge and obtain external evidence before producing or finalizing an answer.","Working definition",{},{"id":260,"data":1131,"type":264,"tunes":1133},{"title":1132,"maxLevel":263,"minLevel":218},"Contents",{},{"id":267,"data":1135,"type":42,"tunes":1137},{"text":1136,"level":218},"What This Really Means",{},{"id":272,"data":1139,"type":224,"tunes":1141},{"text":1140},"An LLM has two fundamentally different ways of obtaining information.",{},{"id":277,"data":1143,"type":224,"tunes":1145},{"text":1144},"The first is model knowledge. This is information represented in the model's learned parameters. No database query, web search or document lookup is required at runtime.",{},{"id":282,"data":1147,"type":224,"tunes":1149},{"text":1148},"The second is runtime knowledge. This is information provided while the model is operating: search results, database records, documents, APIs, user files, tool outputs or other retrieved evidence.",{},{"id":287,"data":1151,"type":224,"tunes":1153},{"text":1152},"RAG connects these two worlds. But RAG itself does not answer the question of when that connection should be activated. That is the purpose of the Retrieval Trigger.",{},{"id":292,"data":1155,"type":295,"tunes":1156},{"code":294},{},{"id":298,"data":1158,"type":224,"tunes":1160},{"text":1159},"The Retrieval Trigger therefore sits before retrieval. The Answer Validity Boundary sits later.",{},{"id":303,"data":1162,"type":224,"tunes":1164},{"text":1163},"The first asks: Do I need external evidence?",{},{"id":308,"data":1166,"type":224,"tunes":1168},{"text":1167},"The second asks: Do I now have enough evidence to support this answer?",{},{"id":313,"data":1170,"type":224,"tunes":1172},{"text":1171},"These are related decisions, but they are not the same decision.",{},{"id":318,"data":1174,"type":42,"tunes":1176},{"text":1175,"level":218},"Simplest Example",{},{"id":323,"data":1178,"type":224,"tunes":1180},{"text":1179},"Consider three questions.",{},{"id":328,"data":1182,"type":346,"tunes":1199},{"content":1183,"stretched":43,"withHeadings":14},[1184,1187,1191,1195],[217,1185,1186],"Internal knowledge","Retrieval Trigger",[1188,1189,1190],"What is the capital of France?","Usually sufficient","No strong trigger",[1192,1193,1194],"What is the current NVIDIA stock price?","Potentially outdated","Trigger retrieval",[1196,1197,1198],"Does this new scientific paper prove that X causes Y?","Cannot establish the claim without examining the evidence","Strong retrieval trigger",{},{"id":349,"data":1201,"type":224,"tunes":1203},{"text":1202},"The first question is based on a highly stable fact.",{},{"id":354,"data":1205,"type":295,"tunes":1206},{"code":356},{},{"id":359,"data":1208,"type":224,"tunes":1210},{"text":1209},"Retrieving documents before answering would usually add little value.",{},{"id":364,"data":1212,"type":224,"tunes":1214},{"text":1213},"Now consider a question whose answer changes continuously.",{},{"id":369,"data":1216,"type":295,"tunes":1217},{"code":371},{},{"id":374,"data":1219,"type":224,"tunes":1221},{"text":1220},"The model may know a great deal about NVIDIA. That does not mean it knows the price now.",{},{"id":379,"data":1223,"type":224,"tunes":1225},{"text":1224},"The third example is even more important.",{},{"id":384,"data":1227,"type":295,"tunes":1228},{"code":386},{},{"id":389,"data":1230,"type":224,"tunes":1232},{"text":1231},"The model's reasoning capability may be perfectly useful. The missing component is evidence.",{},{"id":394,"data":1234,"type":224,"tunes":1236},{"text":1235},"That distinction is fundamental.",{},{"id":399,"data":1238,"type":42,"tunes":1240},{"text":1239,"level":218},"Where the Example Stops Working",{},{"id":404,"data":1242,"type":224,"tunes":1244},{"text":1243},"The examples above make the decision appear binary: retrieve or do not retrieve.",{},{"id":409,"data":1246,"type":224,"tunes":1248},{"text":1247},"Real systems are more complicated. A question may contain several claims, some stable and some current. Retrieved documents may disagree. A retriever may return irrelevant information. The relevant information may exist but fail to rank highly enough. A document may be authoritative but outdated.",{},{"id":414,"data":1250,"type":224,"tunes":1252},{"text":1251},"Retrieval itself can also introduce incorrect context into an otherwise reasonable answer.",{},{"id":419,"data":1254,"type":224,"tunes":1256},{"text":1255},"This is why retrieval should not be treated as an automatic synonym for truth.",{},{"id":424,"data":1258,"type":224,"tunes":1260},{"text":1259},"Research on adaptive retrieval has increasingly moved away from the assumption that every query should receive the same retrieval strategy.",{},{"id":429,"data":1262,"type":224,"tunes":1264},{"text":1263},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa>, for example, explicitly explores retrieval on demand rather than indiscriminately retrieving a fixed number of passages for every input. The authors discuss how unnecessary or irrelevant retrieval can reduce answer quality.",{},{"id":434,"data":1266,"type":224,"tunes":1268},{"text":1267},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> similarly selects between no retrieval, single-step retrieval and more complex retrieval strategies according to question complexity.",{},{"id":439,"data":1270,"type":224,"tunes":1272},{"text":1271},"So the important question is not: Does this system have RAG?",{},{"id":444,"data":1274,"type":224,"tunes":1276},{"text":1275},"It is: Can this system recognize when retrieval is necessary and what kind of retrieval is appropriate?",{},{"id":449,"data":1278,"type":42,"tunes":1280},{"text":1279,"level":218},"Direct Answer",{},{"id":454,"data":1282,"type":224,"tunes":1284},{"text":1283},"An AI should trigger retrieval when answering requires information that its internal model knowledge cannot safely provide with the required freshness, specificity, provenance or evidential support.",{},{"id":459,"data":1286,"type":224,"tunes":1288},{"text":1287},"In practical systems, a Retrieval Trigger can emerge from several conditions:",{},{"id":464,"data":1290,"type":295,"tunes":1291},{"code":466},{},{"id":469,"data":1293,"type":224,"tunes":1295},{"text":1294},"If none of these conditions is materially present, retrieval may be unnecessary. If one or more are present, external evidence becomes part of the answer-generation process.",{},{"id":474,"data":1297,"type":42,"tunes":1299},{"text":1298,"level":218},"Why This Is So",{},{"id":479,"data":1301,"type":224,"tunes":1303},{"text":1302},"A language model's internal knowledge is often described as parametric knowledge. It was learned during training and encoded into the model's parameters.",{},{"id":484,"data":1305,"type":224,"tunes":1307},{"text":1306},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">Lewis et al.'s original RAG work\u003C\u002Fa> framed retrieval as a combination of this parametric memory with external, non-parametric memory. The external memory can be searched and updated without retraining the entire language model.",{},{"id":489,"data":1309,"type":224,"tunes":1311},{"text":1310},"This distinction creates an unavoidable systems problem.",{},{"id":494,"data":1313,"type":224,"tunes":1315},{"text":1314},"The model can know things. But the model cannot assume that everything it knows is current, complete, specific enough and supported by the required evidence.",{},{"id":499,"data":1317,"type":224,"tunes":1319},{"text":1318},"A model can therefore produce a linguistically convincing answer while still operating beyond the point where its internal knowledge is sufficient.",{},{"id":504,"data":1321,"type":224,"tunes":1323},{"text":1322},"That point is where a Retrieval Trigger becomes useful.",{},{"id":509,"data":1325,"type":42,"tunes":1327},{"text":1326,"level":218},"Context",{},{"id":514,"data":1329,"type":224,"tunes":1331},{"text":1330},"Traditional RAG often looks like this:",{},{"id":519,"data":1333,"type":295,"tunes":1334},{"code":521},{},{"id":524,"data":1336,"type":224,"tunes":1338},{"text":1337},"This architecture assumes retrieval before generation. That works well for many knowledge-intensive applications, but it can also perform unnecessary retrieval.",{},{"id":529,"data":1340,"type":224,"tunes":1342},{"text":1341},"More advanced approaches introduce an adaptive step:",{},{"id":534,"data":1344,"type":295,"tunes":1345},{"code":536},{},{"id":539,"data":1347,"type":224,"tunes":1349},{"text":1348},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> goes further by considering retrieval during generation itself. It uses upcoming generation and low-confidence tokens as signals for retrieving additional information.",{},{"id":544,"data":1351,"type":224,"tunes":1353},{"text":1352},"Self-RAG similarly introduces mechanisms allowing retrieval, generation and critique to interact instead of treating retrieval as an unconditional preprocessing step.",{},{"id":549,"data":1355,"type":224,"tunes":1357},{"text":1356},"Adaptive-RAG approaches the same broader problem from query complexity: different questions may require different retrieval strategies.",{},{"id":554,"data":1359,"type":224,"tunes":1361},{"text":1360},"These approaches differ technically. But they expose the same architectural insight: Retrieval should be a decision, not merely a permanent switch.",{},{"id":559,"data":1363,"type":42,"tunes":1365},{"text":1364,"level":218},"Assumptions",{},{"id":564,"data":1367,"type":224,"tunes":1369},{"text":1368},"The Retrieval Trigger framework assumes that a system has access to at least one external information source when retrieval is required.",{},{"id":569,"data":1371,"type":224,"tunes":1373},{"text":1372},"That source could be web search, a document store, vector database, SQL database, knowledge graph, API, enterprise system, user-uploaded document or tool output.",{},{"id":574,"data":1375,"type":224,"tunes":1377},{"text":1376},"It also assumes that retrieval has a cost. That cost does not have to be financial.",{},{"id":579,"data":1379,"type":224,"tunes":1381},{"text":1380},"Retrieval introduces latency, token consumption, context usage, infrastructure complexity and the possibility of retrieving misleading information.",{},{"id":584,"data":1383,"type":224,"tunes":1385},{"text":1384},"The optimal system therefore does not maximize retrieval. It maximizes appropriate retrieval.",{},{"id":589,"data":1387,"type":42,"tunes":1388},{"text":591,"level":218},{},{"id":594,"data":1390,"type":224,"tunes":1392},{"text":1391},"A practical Retrieval Trigger can consider five primary variables.",{},{"id":599,"data":1394,"type":42,"tunes":1396},{"text":1395,"level":263},"Freshness",{},{"id":604,"data":1398,"type":224,"tunes":1400},{"text":1399},"How likely is the required information to have changed? The capital of France has very low volatility. A stock price has extremely high volatility.",{},{"id":609,"data":1402,"type":42,"tunes":1404},{"text":1403,"level":263},"Specificity",{},{"id":614,"data":1406,"type":224,"tunes":1408},{"text":1407},"Does the question require information from a particular source, document, organization, account or dataset? If the user asks what a specific contract says, general model knowledge is irrelevant. The contract must be retrieved.",{},{"id":619,"data":1410,"type":42,"tunes":1412},{"text":1411,"level":263},"Evidence Requirement",{},{"id":624,"data":1414,"type":224,"tunes":1416},{"text":1415},"Does the answer need provenance? A model may know that a claim is generally accepted but still need a source when the task requires verification.",{},{"id":629,"data":1418,"type":42,"tunes":1420},{"text":1419,"level":263},"Knowledge Coverage",{},{"id":634,"data":1422,"type":224,"tunes":1424},{"text":1423},"Is the subject likely to be represented adequately in internal model knowledge? Rare, proprietary, highly local or newly published information creates stronger retrieval pressure.",{},{"id":639,"data":1426,"type":42,"tunes":1428},{"text":1427,"level":263},"Consequence of Error",{},{"id":644,"data":1430,"type":224,"tunes":1432},{"text":1431},"Not every incorrect answer has the same impact. Where factual accuracy materially affects a decision, the acceptable evidence threshold may be higher.",{},{"id":649,"data":1434,"type":224,"tunes":1436},{"text":1435},"These variables do not have to be implemented as literal numeric scores. They describe the decision surface.",{},{"id":654,"data":1438,"type":42,"tunes":1440},{"text":1439,"level":218},"Diagnostic \u002F Decision Method",{},{"id":659,"data":1442,"type":224,"tunes":1444},{"text":1443},"A very simple Retrieval Trigger can be implemented without machine learning.",{},{"id":664,"data":1446,"type":295,"tunes":1447},{"code":666},{},{"id":669,"data":1449,"type":224,"tunes":1451},{"text":1450},"For a stable factual question:",{},{"id":674,"data":1453,"type":295,"tunes":1454},{"code":676},{},{"id":679,"data":1456,"type":224,"tunes":1458},{"text":1457},"For a current stock price:",{},{"id":684,"data":1460,"type":295,"tunes":1461},{"code":686},{},{"id":689,"data":1463,"type":224,"tunes":1465},{"text":1464},"For a scientific claim:",{},{"id":694,"data":1467,"type":295,"tunes":1468},{"code":696},{},{"id":699,"data":1470,"type":224,"tunes":1472},{"text":1471},"Production systems can make this decision far more sophisticated. A classifier could predict retrieval requirements. A model could emit special control tokens. A router could classify query complexity. Retrieval could also be triggered repeatedly during generation.",{},{"id":704,"data":1474,"type":224,"tunes":1476},{"text":1475},"The implementation can change. The architectural question remains the same:",{},{"id":709,"data":1478,"type":257,"tunes":1480},{"text":1479,"caption":712,"alignment":256},"Is the evidence currently available to the model sufficient for the answer it is about to produce?",{},{"id":715,"data":1482,"type":42,"tunes":1484},{"text":1483,"level":218},"Evidence",{},{"id":720,"data":1486,"type":224,"tunes":1488},{"text":1487},"The concept proposed here is consistent with several lines of retrieval research.",{},{"id":725,"data":1490,"type":224,"tunes":1492},{"text":1491},"The original \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">RAG architecture\u003C\u002Fa> demonstrated the usefulness of combining parametric model knowledge with external non-parametric knowledge, particularly for knowledge-intensive tasks.",{},{"id":730,"data":1494,"type":224,"tunes":1496},{"text":1495},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> explicitly explores active retrieval during generation, including retrieval prompted by low-confidence upcoming content.",{},{"id":735,"data":1498,"type":224,"tunes":1500},{"text":1499},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa> demonstrates an architecture in which retrieval can occur on demand and is followed by reflection on retrieved passages and generated content.",{},{"id":740,"data":1502,"type":224,"tunes":1504},{"text":1503},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> dynamically chooses among different strategies according to question complexity, including situations where no retrieval is required.",{},{"id":745,"data":1506,"type":224,"tunes":1508},{"text":1507},"The term Retrieval Trigger is used here as a system-level abstraction over this broader family of decisions.",{},{"id":750,"data":1510,"type":224,"tunes":1512},{"text":1511},"It does not claim that these papers use the same terminology. Instead, it identifies the shared architectural problem: What causes an AI system to transition from internal knowledge to external evidence?",{},{"id":755,"data":1514,"type":42,"tunes":1516},{"text":1515,"level":218},"Real Examples",{},{"id":760,"data":1518,"type":224,"tunes":1520},{"text":1519},"Consider a support assistant connected to a company's documentation.",{},{"id":765,"data":1522,"type":295,"tunes":1523},{"code":767},{},{"id":770,"data":1525,"type":224,"tunes":1527},{"text":1526},"If the procedure is stable and reliably represented in the assistant's current instructions, direct answering may be appropriate.",{},{"id":775,"data":1529,"type":295,"tunes":1530},{"code":777},{},{"id":780,"data":1532,"type":224,"tunes":1534},{"text":1533},"That information is user-specific and dynamic. The Retrieval Trigger fires. The system must inspect the actual account or authorization data.",{},{"id":785,"data":1536,"type":295,"tunes":1537},{"code":787},{},{"id":790,"data":1539,"type":224,"tunes":1541},{"text":1540},"The model can understand deployment systems and explain common reasons. But the question is asking about a particular event. Logs, CI\u002FCD output or incident records are required.",{},{"id":795,"data":1543,"type":224,"tunes":1545},{"text":1544},"The same logic works for web search.",{},{"id":800,"data":1547,"type":295,"tunes":1548},{"code":802},{},{"id":805,"data":1550,"type":224,"tunes":1552},{"text":1551},"A general explanation may not require retrieval.",{},{"id":810,"data":1554,"type":295,"tunes":1555},{"code":812},{},{"id":815,"data":1557,"type":224,"tunes":1559},{"text":1558},"Now source-specific evidence is required.",{},{"id":820,"data":1561,"type":295,"tunes":1562},{"code":822},{},{"id":825,"data":1564,"type":224,"tunes":1566},{"text":1565},"This introduces a freshness requirement as well. The underlying subject has not changed. The information requirement has.",{},{"id":830,"data":1568,"type":42,"tunes":1570},{"text":1569,"level":218},"Common Misconceptions and Failure Modes",{},{"id":835,"data":1572,"type":224,"tunes":1574},{"text":1573},"More retrieval automatically produces a better answer. It does not. Irrelevant documents consume context and can distract generation.",{},{"id":840,"data":1576,"type":224,"tunes":1578},{"text":1577},"High model confidence means retrieval is unnecessary. A model can produce an incorrect answer confidently. Self-reported confidence should therefore not be treated as the only trigger.",{},{"id":845,"data":1580,"type":224,"tunes":1582},{"text":1581},"Successful retrieval means the answer is verified. Retrieval only provides candidate evidence. The evidence must still be relevant, sufficiently authoritative and correctly interpreted.",{},{"id":850,"data":1584,"type":224,"tunes":1586},{"text":1585},"RAG automatically solves outdated knowledge. It only does so if the retrieval corpus itself contains current information. Retrieving an outdated document does not create a current answer.",{},{"id":855,"data":1588,"type":224,"tunes":1590},{"text":1589},"One retrieval step is always enough. Complex questions may require several pieces of evidence or iterative retrieval.",{},{"id":860,"data":1592,"type":42,"tunes":1594},{"text":1593,"level":218},"Edge Cases",{},{"id":865,"data":1596,"type":224,"tunes":1598},{"text":1597},"Some questions contain both stable and unstable information.",{},{"id":870,"data":1600,"type":295,"tunes":1601},{"code":872},{},{"id":875,"data":1603,"type":224,"tunes":1605},{"text":1604},"The first part may be answerable from stable model knowledge. The second part requires current information.",{},{"id":880,"data":1607,"type":224,"tunes":1609},{"text":1608},"A sufficiently capable system should not necessarily treat the entire query as one retrieval decision. It can trigger retrieval only where required.",{},{"id":885,"data":1611,"type":224,"tunes":1613},{"text":1612},"Another edge case is disagreement between sources. Suppose retrieval returns three documents making incompatible claims.",{},{"id":890,"data":1615,"type":224,"tunes":1617},{"text":1616},"The Retrieval Trigger has already succeeded: the system recognized that external evidence was required. But the task is not finished.",{},{"id":895,"data":1619,"type":224,"tunes":1621},{"text":1620},"The system has now reached an evidence evaluation problem. This is where the Answer Validity Boundary becomes important.",{},{"id":900,"data":1623,"type":224,"tunes":1625},{"text":1624},"The system may have retrieved information and still not possess enough evidence to make a strong conclusion.",{},{"id":905,"data":1627,"type":295,"tunes":1628},{"code":907},{},{"id":910,"data":1630,"type":224,"tunes":1632},{"text":1631},"The trigger obtains evidence. The validity boundary determines whether that evidence is sufficient.",{},{"id":915,"data":1634,"type":42,"tunes":1636},{"text":1635,"level":218},"Limitations",{},{"id":920,"data":1638,"type":224,"tunes":1640},{"text":1639},"The Retrieval Trigger is a conceptual framework, not a universal algorithm.",{},{"id":925,"data":1642,"type":224,"tunes":1644},{"text":1643},"Different systems will require different trigger rules. A customer-support bot, scientific research assistant, search engine and autonomous software agent do not have identical evidence requirements.",{},{"id":930,"data":1646,"type":224,"tunes":1648},{"text":1647},"Trigger thresholds can also create their own failure modes. A threshold that is too low causes excessive retrieval. A threshold that is too high causes unsupported answering.",{},{"id":935,"data":1650,"type":224,"tunes":1652},{"text":1651},"The retrieval infrastructure itself also matters. A perfect trigger connected to a poor source collection still produces poor evidence.",{},{"id":940,"data":1654,"type":224,"tunes":1656},{"text":1655},"Similarly, an excellent knowledge base provides little value if the trigger never activates when it is needed.",{},{"id":945,"data":1658,"type":224,"tunes":1660},{"text":1659},"The Retrieval Trigger therefore solves only one part of a larger architecture.",{},{"id":950,"data":1662,"type":42,"tunes":1664},{"text":1663,"level":218},"What Would Change This Answer?",{},{"id":955,"data":1666,"type":224,"tunes":1668},{"text":1667},"Future models may contain better mechanisms for identifying their own knowledge limitations. Retrievers may become cheaper and faster. Long-context systems may carry far more source material continuously.",{},{"id":960,"data":1670,"type":224,"tunes":1672},{"text":1671},"Models may also increasingly combine search, databases, tools and structured knowledge without exposing a distinct RAG stage to the application developer.",{},{"id":965,"data":1674,"type":224,"tunes":1676},{"text":1675},"These changes could alter how the trigger is implemented. They do not necessarily remove the underlying decision.",{},{"id":970,"data":1678,"type":224,"tunes":1680},{"text":1679},"As long as there is a difference between information already available to the model and information that must be obtained externally, a system still needs some mechanism for determining when to cross that boundary.",{},{"id":975,"data":1682,"type":224,"tunes":1684},{"text":1683},"The implementation may disappear from view. The architectural question remains.",{},{"id":980,"data":1686,"type":42,"tunes":1687},{"text":982,"level":218},{},{"id":985,"data":1689,"type":224,"tunes":1691},{"text":1690},"RAG begins too late to explain the whole problem.",{},{"id":990,"data":1693,"type":224,"tunes":1695},{"text":1694},"Before retrieval can happen, an AI system must determine whether retrieval is necessary. That decision is the Retrieval Trigger.",{},{"id":995,"data":1697,"type":295,"tunes":1698},{"code":997},{},{"id":1000,"data":1700,"type":224,"tunes":1702},{"text":1701},"But the broader implication is more important. Reliable AI does not merely need access to knowledge. It needs a method for determining when its current knowledge is insufficient.",{},{"id":1005,"data":1704,"type":295,"tunes":1705},{"code":1007},{},{"id":1010,"data":1707,"type":224,"tunes":1709},{"text":1708},"The Retrieval Trigger determines when the system should seek evidence. The Answer Validity Boundary determines whether that evidence is sufficient.",{},{"id":1015,"data":1711,"type":224,"tunes":1713},{"text":1712},"Together they describe something more useful than RAG alone: a decision process for moving from what an AI appears to know toward what it can actually support.",{},{"id":1020,"data":1715,"type":42,"tunes":1717},{"text":1716,"level":218},"Primary Sources",{},{"id":1025,"data":1719,"type":224,"tunes":1721},{"text":1720},"Patrick Lewis et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\u003C\u002Fa> (2020). Foundational RAG work describing the combination of parametric model memory with external non-parametric memory.",{},{"id":1030,"data":1723,"type":224,"tunes":1725},{"text":1724},"Zhengbao Jiang et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">Active Retrieval Augmented Generation\u003C\u002Fa> (2023). Introduces FLARE and active retrieval during generation, including retrieval based on low-confidence predicted content.",{},{"id":1035,"data":1727,"type":224,"tunes":1729},{"text":1728},"Akari Asai et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection\u003C\u002Fa> (2023). Explores adaptive retrieval on demand and self-reflection instead of unconditional fixed retrieval.",{},{"id":1040,"data":1731,"type":224,"tunes":1733},{"text":1732},"Soyeong Jeong et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity\u003C\u002Fa> (2024). Dynamically selects among no retrieval, single-step retrieval and more complex retrieval strategies according to the incoming question.",{},"2.31.6","An AI model does not need retrieval for every question. The important problem is knowing when its internal knowledge is no longer enough. The Retrieval Trigger is a practical decision boundary that determines when an AI system should stop relying solely on model knowledge and obtain external evidence before 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