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投资回报率","\u002Fportfolio\u002Ffrom-vision-to-value-maximizing-pos-roi-through-consistent-ci-creation-and-strategic-branding","i-lucide-briefcase-business",11,[],{"statusCode":4,"data":222,"message":1601},{"id":223,"title":224,"slug":225,"content":226,"contentJson":227,"excerpt":799,"featuredImage":800,"featuredImageAlt":801,"featuredImageCaption":10,"featuredImageTitle":10,"featuredImageCopyright":10,"featuredImageAuthor":10,"featuredImageSourceUrl":10,"featuredImageLicense":10,"featuredImageIsAiGenerated":43,"status":802,"publishedAt":803,"createdAt":804,"updatedAt":805,"seoLocalePaths":806,"categories":815,"author":824,"translations":828},"498","Les preuves peuvent-elles survivre au pipeline d’IA ? — La chaîne de préservation des preuves","can-evidence-survive-the-ai-pipeline-the-evidence-preservation-chain","\u003Ch2 id=\"section-1\">Question\u003C\u002Fh2>\n\u003Cp>Que se passe-t-il lorsque la source originale est correcte, mais que le pipeline d'IA supprime les informations nécessaires pour comprendre correctement cette source ?\u003C\u002Fp>\n\u003Cp>Un document fiable peut devenir un contexte non fiable si l'analyse supprime sa structure de tableau, le découpage sépare une condition de son exception, la normalisation abandonne les unités, la récupération perd les métadonnées de version ou l'assemblage du contexte masque un conflit.\u003C\u002Fp>\n\u003Cp>Cet article utilise le terme Chaîne de préservation des preuves pour désigner l'exigence que les preuves conservent les propriétés nécessaires à une interprétation correcte lorsqu'elles passent de la source au contexte final de l'IA.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Une Chaîne de préservation des preuves est la séquence de transformations de bout en bout à travers laquelle les preuves doivent conserver leur identité, leur provenance, leur portée, leur signification et leur validité avant de pouvoir étayer en toute sécurité une affirmation générée par l'IA.\u003Ccite class=\"block mt-2 text-sm\">— Définition de travail — un concept architectural introduit ici, et non un terme de recherche standardisé.\u003C\u002Fcite>\u003C\u002Fblockquote>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Sommaire\">\u003Cstrong class=\"editorjs-toc__title\">Sommaire\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-7\" class=\"editorjs-toc__link\">Ce que cela signifie vraiment\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-12\" class=\"editorjs-toc__link\">Exemple le plus simple\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-20\" class=\"editorjs-toc__link\">Où l'exemple cesse de fonctionner\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-24\" class=\"editorjs-toc__link\">Réponse directe\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-27\" class=\"editorjs-toc__link\">Pourquoi le RAG peut échouer même avec la bonne source\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-31\" class=\"editorjs-toc__link\">Les huit points de préservation\u003C\u002Fa>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-1\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-32\" class=\"editorjs-toc__link\">1. Identité\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-34\" class=\"editorjs-toc__link\">2. Provenance\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-36\" class=\"editorjs-toc__link\">3. Structure\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-38\" class=\"editorjs-toc__link\">4. Unités\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-40\" class=\"editorjs-toc__link\">5. Conditions et exceptions\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-42\" class=\"editorjs-toc__link\">6. Version et temps\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-44\" class=\"editorjs-toc__link\">7. État de conflit\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-46\" class=\"editorjs-toc__link\">8. Portée d'accès\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-48\" class=\"editorjs-toc__link\">Des données brutes aux preuves contrôlées\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-52\" class=\"editorjs-toc__link\">L'ingénierie du contexte fait partie de la chaîne\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-56\" class=\"editorjs-toc__link\">Lien avec la distance probatoire\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-60\" class=\"editorjs-toc__link\">Lien avec la couche d'autorité de récupération\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-64\" class=\"editorjs-toc__link\">Modèle de données pratique\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-68\" class=\"editorjs-toc__link\">Méthode de diagnostic \u002F de décision\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-72\" class=\"editorjs-toc__link\">Mode de défaillance : le fragment orphelin\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-76\" class=\"editorjs-toc__link\">Mode de défaillance : Normalisation par invention\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-80\" class=\"editorjs-toc__link\">Mode de défaillance : Effondrement des conflits\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-84\" class=\"editorjs-toc__link\">Mode de défaillance : Compression du contexte\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-88\" class=\"editorjs-toc__link\">Cas limite : La compression avec perte peut rester correcte\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-92\" class=\"editorjs-toc__link\">Limites\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-96\" class=\"editorjs-toc__link\">Qu'est-ce qui changerait cette réponse ?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-99\" class=\"editorjs-toc__link\">Conclusion\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-104\" class=\"editorjs-toc__link\">Sources primaires\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-7\">Ce que cela signifie vraiment\u003C\u002Fh2>\n\u003Cp>La plupart des discussions sur le RAG se concentrent sur la question de savoir si des informations pertinentes peuvent être trouvées.\u003C\u002Fp>\n\u003Cp>Mais les preuves passent généralement par plusieurs transformations avant que le modèle ne les voie.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Original Source\n   ↓\nParsing\n   ↓\nNormalization\n   ↓\nChunking\n   ↓\nIndexing\n   ↓\nRetrieval\n   ↓\nReranking\n   ↓\nContext Assembly\n   ↓\nModel\n   ↓\nClaim\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Les preuves peuvent se dégrader à n'importe quel point de cette chaîne, même lorsque le document original était parfaitement correct.\u003C\u002Fp>\n\u003Ch2 id=\"section-12\">Exemple le plus simple\u003C\u002Fh2>\n\u003Cp>Imaginez qu'un manuel de produit contienne cette affirmation :\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Température maximale de fonctionnement : 40 °C, sauf lorsque l'accessoire X est installé, auquel cas la limite est de 35 °C.\u003Ccite class=\"block mt-2 text-sm\">— Exemple d'énoncé source\u003C\u002Fcite>\u003C\u002Fblockquote>\n\u003Cp>Un processus de découpage médiocre divise la phrase.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Chunk A:\nMaximum operating temperature: 40 °C.\n\nChunk B:\nExcept when accessory X is installed, in which case the limit is 35 °C.\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Le récupérateur ne renvoie que le Fragment A.\u003C\u002Fp>\n\u003Cp>Le texte récupéré n'est pas fabriqué. Il provient directement du manuel. Pourtant, le contexte est incomplet exactement de la manière qui compte.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Correct source\n        +\nlossy transformation\n        =\nmisleading evidence\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Ch2 id=\"section-20\">Où l'exemple cesse de fonctionner\u003C\u002Fh2>\n\u003Cp>La préservation des preuves ne signifie pas copier chaque octet source inchangé dans le contexte du modèle.\u003C\u002Fp>\n\u003Cp>La compression, la normalisation, le découpage en segments et la synthèse sont souvent nécessaires. L'objectif est de préserver les propriétés requises pour une interprétation correcte, et non d'éliminer la transformation.\u003C\u002Fp>\n\u003Cp>La question d'ingénierie difficile est donc : quelles propriétés sont sémantiquement porteuses ?\u003C\u002Fp>\n\u003Ch2 id=\"section-24\">Réponse directe\u003C\u002Fh2>\n\u003Cp>Une preuve survit à un pipeline d'IA lorsque les transformations entre la source et le modèle préservent suffisamment d'identité, de provenance, de structure, d'unités, de conditions, de conflits et de métadonnées de validité pour que l'information récupérée signifie la même chose en contexte qu'à la source.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Evidence Preservation\n=\nmeaning\n+ identity\n+ provenance\n+ scope\n+ units\n+ conditions\n+ validity\n+ traceability\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Ch2 id=\"section-27\">Pourquoi le RAG peut échouer même avec la bonne source\u003C\u002Fh2>\n\u003Cp>Un récupérateur peut trouver le bon document tout en fournissant la mauvaise unité de preuve.\u003C\u002Fp>\n\u003Cp>Le problème peut être survenu bien plus tôt : lors de l'OCR, de l'analyse de documents, de l'extraction HTML, de la conversion de tableaux, du mappage de champs, de la construction de segments ou de la perte de métadonnées.\u003C\u002Fp>\n\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fwhere-does-an-llm-get-its-data-rag-data-sources-in-python\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Où un LLM obtient-il ses données ? Sources de données RAG en Python\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Le RAG commence par des sources de données externes et des méthodes de récupération. La préservation des preuves demande ce qui doit survivre entre la source originale et le contexte récupéré.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ch2 id=\"section-31\">Les huit points de préservation\u003C\u002Fh2>\n\u003Ch3 id=\"section-32\">1. Identité\u003C\u002Fh3>\n\u003Cp>Le pipeline doit préserver quelle entité, produit, document, utilisateur, contrat ou enregistrement la preuve décrit réellement.\u003C\u002Fp>\n\u003Ch3 id=\"section-34\">2. Provenance\u003C\u002Fh3>\n\u003Cp>Le système devrait pouvoir retracer la preuve récupérée jusqu'à sa source originale plutôt que seulement jusqu'à un identifiant de segment anonyme.\u003C\u002Fp>\n\u003Ch3 id=\"section-36\">3. Structure\u003C\u002Fh3>\n\u003Cp>Les tableaux, les titres, les listes, les relations parent-enfant et les exceptions peuvent porter une signification qui disparaît lorsque tout est aplati en fragments de texte indépendants.\u003C\u002Fp>\n\u003Ch3 id=\"section-38\">4. Unités\u003C\u002Fh3>\n\u003Cp>Un nombre sans son unité peut devenir inutile ou dangereux. 100 peut signifier millisecondes, kilogrammes, euros, volts ou pourcentage d'une référence.\u003C\u002Fp>\n\u003Ch3 id=\"section-40\">5. Conditions et exceptions\u003C\u002Fh3>\n\u003Cp>Les règles contiennent fréquemment des conditions d'applicabilité. Séparer la règle de l'exception peut préserver les mots tout en détruisant le sens.\u003C\u002Fp>\n\u003Ch3 id=\"section-42\">6. Version et temps\u003C\u002Fh3>\n\u003Cp>Les preuves doivent préserver le moment où elles sont devenues valides, le moment où elles ont été observées et si un objet plus récent les remplace.\u003C\u002Fp>\n\u003Ch3 id=\"section-44\">7. État de conflit\u003C\u002Fh3>\n\u003Cp>Si deux sources sont en désaccord, la normalisation ne doit pas sélectionner silencieusement une valeur et effacer le fait que le désaccord existait.\u003C\u002Fp>\n\u003Ch3 id=\"section-46\">8. Portée d'accès\u003C\u002Fh3>\n\u003Cp>Les limites de permission et de locataire doivent rester attachées aux preuves tout au long de la récupération et de l'assemblage du contexte.\u003C\u002Fp>\n\u003Ch2 id=\"section-48\">Des données brutes aux preuves contrôlées\u003C\u002Fh2>\n\u003Cp>L'endroit le plus solide pour préserver les preuves est souvent avant la vectorisation.\u003C\u002Fp>\n\u003Cp>Si l'identité de la source, la validité et les conflits sont perdus avant la création des embeddings, la récupération ne peut pas les reconstruire magiquement plus tard.\u003C\u002Fp>\n\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fportfolio\u002Ffrom-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Des données brutes au contexte IA vérifiable : une architecture pratique pour une information prête pour les agents\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">L'architecture commence avant la récupération en préservant l'identité, la provenance, les conflits, les inconnues et la validité dans des objets d'information réutilisables.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ch2 id=\"section-52\">L'ingénierie du contexte fait partie de la chaîne\u003C\u002Fh2>\n\u003Cp>Même des preuves correctement récupérées peuvent être endommagées lors de l'assemblage du contexte.\u003C\u002Fp>\n\u003Cp>Un constructeur de contexte peut tronquer une exception, supprimer les étiquettes de source, fusionner des passages contradictoires, réordonner les dépendances ou dépasser le budget d'attention utile du modèle.\u003C\u002Fp>\n\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fwhat-is-context-engineering-what-the-model-receives-before-it-answers\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Qu'est-ce que l'ingénierie de contexte ? Ce que le modèle reçoit avant de répondre\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">L'ingénierie de contexte détermine quelles informations récupérées parviennent réellement au modèle et sous quelles contraintes structurelles, de validité et de sécurité.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ch2 id=\"section-56\">Lien avec la distance probatoire\u003C\u002Fh2>\n\u003Cp>La préservation des preuves et la distance probatoire décrivent différents modes de défaillance.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Evidence Preservation Chain:\nDid the evidence survive transformation?\n\nEvidence Distance:\nHow far is the final claim from that evidence?\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Un système peut préserver parfaitement les preuves et néanmoins en tirer une inférence non étayée. Inversement, le raisonnement final peut être simple alors que les preuves elles-mêmes étaient déjà corrompues plus tôt dans le pipeline.\u003C\u002Fp>\n\u003Ch2 id=\"section-60\">Lien avec la couche d'autorité de récupération\u003C\u002Fh2>\n\u003Cp>La couche d'autorité de récupération sélectionne la source qui mérite un poids probatoire.\u003C\u002Fp>\n\u003Cp>La chaîne de préservation des preuves garantit que les propriétés conférant son autorité à cette source ne sont pas écartées après la sélection.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Authority at source\n        ↓\nPreservation through pipeline\n        ↓\nAuthority still visible in context\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Ch2 id=\"section-64\">Modèle de données pratique\u003C\u002Fh2>\n\u003Cp>Une unité de récupération peut transporter plus que du texte.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>{\n  "text": "Maximum operating temperature is 40 °C...",\n  "sourceId": "manual-v4.2",\n  "entityId": "device-184",\n  "section": "Operating limits",\n  "validFrom": "2026-03-01",\n  "unit": "°C",\n  "conditions": ["35 °C when accessory X is installed"],\n  "authority": "manufacturer_manual",\n  "conflicts": [],\n  "accessScope": "public"\n}\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Le schéma exact dépend de l'application. Le principe architectural est que le texte récupérable ne doit pas se détacher des métadonnées nécessaires à son interprétation.\u003C\u002Fp>\n\u003Ch2 id=\"section-68\">Méthode de diagnostic \u002F de décision\u003C\u002Fh2>\n\u003Cp>Choisissez une réponse importante et retracez une affirmation justificative à rebours dans le système.\u003C\u002Fp>\n\u003Col>\u003Cli>Identifier l'affirmation finale.\u003C\u002Fli>\u003Cli>Identifier le passage de contexte exact qui l'a étayée.\u003C\u002Fli>\u003Cli>Identifier le fragment récupéré qui a produit ce passage.\u003C\u002Fli>\u003Cli>Identifier l'enregistrement normalisé ou le document analysé derrière le fragment.\u003C\u002Fli>\u003Cli>Identifier la source originale.\u003C\u002Fli>\u003Cli>Comparer le sens, la portée, les unités, les conditions et la version à chaque transition.\u003C\u002Fli>\u003C\u002Fol>\n\u003Cp>Tout point où un sens essentiel disparaît constitue une rupture dans la chaîne de préservation des preuves.\u003C\u002Fp>\n\u003Ch2 id=\"section-72\">Mode de défaillance : le fragment orphelin\u003C\u002Fh2>\n\u003Cp>Un fragment orphelin contient un texte d'apparence utile mais sans relation fiable avec la structure du document, l'identité de la source, la version ou les conditions d'applicabilité dont il provient.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"Refunds are available for 30 days."\n\nMissing:\nWhich product?\nWhich country?\nWhich policy version?\nFrom what effective date?\nWhat exceptions?\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>La phrase peut être authentique et rester une preuve non fiable.\u003C\u002Fp>\n\u003Ch2 id=\"section-76\">Mode de défaillance : Normalisation par invention\u003C\u002Fh2>\n\u003Cp>La normalisation devrait aligner les représentations. Elle ne devrait pas inventer silencieusement des valeurs manquantes pour donner aux enregistrements une apparence complète.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Unknown\n≠\nFalse\n≠\nZero\n≠\nDefault\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Préserver les inconnues fait partie de la préservation des preuves.\u003C\u002Fp>\n\u003Ch2 id=\"section-80\">Mode de défaillance : Effondrement des conflits\u003C\u002Fh2>\n\u003Cp>Supposons qu'une source approuvée indique 24 mois et qu'une autre indique 36 mois.\u003C\u002Fp>\n\u003Cp>Un pipeline qui stocke simplement la dernière valeur importée produit une base de données propre et détruit la preuve qu'un conflit existait.\u003C\u002Fp>\n\u003Cp>Un système vérifiable devrait préserver les deux valeurs, leur provenance et le conflit non résolu jusqu'à ce qu'une règle ou une décision humaine le résolve.\u003C\u002Fp>\n\u003Ch2 id=\"section-84\">Mode de défaillance : Compression du contexte\u003C\u002Fh2>\n\u003Cp>La compression du contexte peut être utile, mais un résumé qui supprime les qualificatifs peut accroître la certitude apparente.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Source:\n"Evidence suggests X under conditions A and B, but the sample is small."\n\nBad compression:\n"X is proven."\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>L'information est devenue plus courte et moins véridique en même temps.\u003C\u002Fp>\n\u003Ch2 id=\"section-88\">Cas limite : La compression avec perte peut rester correcte\u003C\u002Fh2>\n\u003Cp>Toutes les pertes ne sont pas nuisibles.\u003C\u002Fp>\n\u003Cp>Supprimer la mise en forme visuelle d'un paragraphe peut n'avoir aucune conséquence sémantique. Convertir des degrés Celsius en kelvins peut préserver le sens si la transformation est explicite et correcte. Dédupliquer des copies exactes peut améliorer la récupération sans perdre de preuves.\u003C\u002Fp>\n\u003Cp>L'objectif n'est donc pas un stockage sans perte à chaque étape. C'est une préservation sémantiquement suffisante.\u003C\u002Fp>\n\u003Ch2 id=\"section-92\">Limites\u003C\u002Fh2>\n\u003Cp>La chaîne de préservation des preuves est un cadre architectural, et non une garantie que les preuves préservées seront interprétées correctement.\u003C\u002Fp>\n\u003Cp>Un modèle peut recevoir des preuves parfaitement préservées et mal raisonner malgré tout. Un système de récupération peut préserver la provenance et classer néanmoins la mauvaise source. Une source elle-même peut aussi être erronée.\u003C\u002Fp>\n\u003Cp>La préservation protège le pipeline de preuves. Elle ne remplace pas l'évaluation des sources ni le raisonnement.\u003C\u002Fp>\n\u003Ch2 id=\"section-96\">Qu'est-ce qui changerait cette réponse ?\u003C\u002Fh2>\n\u003Cp>Les systèmes futurs pourront récupérer des objets de preuve structurés plutôt que des fragments de texte, préserver automatiquement les graphes de provenance et exposer directement aux modèles et aux agents des contraintes de validité lisibles par machine.\u003C\u002Fp>\n\u003Cp>Cela rendrait la préservation des preuves plus facile à mettre en œuvre, mais rendrait le principe plus important plutôt que moins important.\u003C\u002Fp>\n\u003Ch2 id=\"section-99\">Conclusion\u003C\u002Fh2>\n\u003Cp>Une IA fiable nécessite plus que de trouver une source correcte.\u003C\u002Fp>\n\u003Cp>Les propriétés qui rendent la source utile doivent survivre à l'analyse syntaxique, à la normalisation, au découpage, à la récupération et à l'assemblage du contexte.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Source Authority\n      ↓\nEvidence Preservation Chain\n      ↓\nRetrievable Evidence\n      ↓\nEvidence Distance\n      ↓\nReasoning\n      ↓\nAnswer Validity Boundary\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Si la chaîne se rompt, le modèle peut recevoir des informations techniquement authentiques mais qui ne sont plus sûres à interpréter comme les preuves d'origine.\u003C\u002Fp>\n\u003Ch2 id=\"section-104\">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).\u003C\u002Fp>\n\u003Cp>Nelson F. Liu et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.03172\" target=\"_blank\">Lost in the Middle: How Language Models Use Long Contexts\u003C\u002Fa> (2023).\u003C\u002Fp>\n\u003Cp>W3C, \u003Ca href=\"https:\u002F\u002Fwww.w3.org\u002FTR\u002Fprov-overview\u002F\" target=\"_blank\">PROV Overview\u003C\u002Fa>.\u003C\u002Fp>",{"time":228,"blocks":229,"version":798},1792096551882,[230,236,242,247,252,260,267,272,277,282,288,293,298,303,309,314,319,324,329,334,339,344,349,354,359,364,369,374,379,384,395,400,405,410,415,420,425,430,435,440,445,450,455,460,465,470,475,480,485,490,495,504,509,514,519,528,533,538,543,548,553,558,563,568,573,578,583,588,593,598,613,618,623,628,633,638,643,648,653,658,663,668,673,678,683,688,693,698,703,708,713,718,723,728,733,738,743,748,753,758,763,768,773,778,783,788,793],{"id":231,"data":232,"type":42,"tunes":235},"tP10jlMV2G",{"text":233,"level":234},"Question",2,{},{"id":237,"data":238,"type":240,"tunes":241},"zD8a-5x7r9",{"text":239},"Que se passe-t-il lorsque la source originale est correcte, mais que le pipeline d'IA supprime les informations nécessaires pour comprendre correctement cette source ?","paragraph",{},{"id":243,"data":244,"type":240,"tunes":246},"LJjTjSV74R",{"text":245},"Un document fiable peut devenir un contexte non fiable si l'analyse supprime sa structure de tableau, le découpage sépare une condition de son exception, la normalisation abandonne les unités, la récupération perd les métadonnées de version ou l'assemblage du contexte masque un conflit.",{},{"id":248,"data":249,"type":240,"tunes":251},"kOzPNiKvzu",{"text":250},"Cet article utilise le terme Chaîne de préservation des preuves pour désigner l'exigence que les preuves conservent les propriétés nécessaires à une interprétation correcte lorsqu'elles passent de la source au contexte final de l'IA.",{},{"id":253,"data":254,"type":258,"tunes":259},"xUf9Qt8UHn",{"text":255,"caption":256,"alignment":257},"Une Chaîne de préservation des preuves est la séquence de transformations de bout en bout à travers laquelle les preuves doivent conserver leur identité, leur provenance, leur portée, leur signification et leur validité avant de pouvoir étayer en toute sécurité une affirmation générée par l'IA.","Définition de travail — un concept architectural introduit ici, et non un terme de recherche standardisé.","left","quote",{},{"id":261,"data":262,"type":265,"tunes":266},"RxQsaJsNo6",{"title":263,"maxLevel":264,"minLevel":234},"Sommaire",3,"tableOfContents",{},{"id":268,"data":269,"type":42,"tunes":271},"NBMzNZ_qHB",{"text":270,"level":234},"Ce que cela signifie vraiment",{},{"id":273,"data":274,"type":240,"tunes":276},"zAEKGXCEmK",{"text":275},"La plupart des discussions sur le RAG se concentrent sur la question de savoir si des informations pertinentes peuvent être trouvées.",{},{"id":278,"data":279,"type":240,"tunes":281},"i2gu8IM3j1",{"text":280},"Mais les preuves passent généralement par plusieurs transformations avant que le modèle ne les voie.",{},{"id":283,"data":284,"type":286,"tunes":287},"lN52dh3dxK",{"code":285},"Original Source\n   ↓\nParsing\n   ↓\nNormalization\n   ↓\nChunking\n   ↓\nIndexing\n   ↓\nRetrieval\n   ↓\nReranking\n   ↓\nContext Assembly\n   ↓\nModel\n   ↓\nClaim","code",{},{"id":289,"data":290,"type":240,"tunes":292},"G90TdnX4WC",{"text":291},"Les preuves peuvent se dégrader à n'importe quel point de cette chaîne, même lorsque le document original était parfaitement correct.",{},{"id":294,"data":295,"type":42,"tunes":297},"mC_OsF2ji3",{"text":296,"level":234},"Exemple le plus simple",{},{"id":299,"data":300,"type":240,"tunes":302},"P8XUivVa7g",{"text":301},"Imaginez qu'un manuel de produit contienne cette affirmation :",{},{"id":304,"data":305,"type":258,"tunes":308},"2LamDXESHK",{"text":306,"caption":307,"alignment":257},"Température maximale de fonctionnement : 40 °C, sauf lorsque l'accessoire X est installé, auquel cas la limite est de 35 °C.","Exemple d'énoncé source",{},{"id":310,"data":311,"type":240,"tunes":313},"BNY1TVLg3y",{"text":312},"Un processus de découpage médiocre divise la phrase.",{},{"id":315,"data":316,"type":286,"tunes":318},"xaAZ_aJazc",{"code":317},"Chunk A:\nMaximum operating temperature: 40 °C.\n\nChunk B:\nExcept when accessory X is installed, in which case the limit is 35 °C.",{},{"id":320,"data":321,"type":240,"tunes":323},"q0ORvmk__K",{"text":322},"Le récupérateur ne renvoie que le Fragment A.",{},{"id":325,"data":326,"type":240,"tunes":328},"MupQI-_xIz",{"text":327},"Le texte récupéré n'est pas fabriqué. Il provient directement du manuel. Pourtant, le contexte est incomplet exactement de la manière qui compte.",{},{"id":330,"data":331,"type":286,"tunes":333},"cYEvuH7MR6",{"code":332},"Correct source\n        +\nlossy transformation\n        =\nmisleading evidence",{},{"id":335,"data":336,"type":42,"tunes":338},"F_ZvT1AfTN",{"text":337,"level":234},"Où l'exemple cesse de fonctionner",{},{"id":340,"data":341,"type":240,"tunes":343},"flP5_wVcvt",{"text":342},"La préservation des preuves ne signifie pas copier chaque octet source inchangé dans le contexte du modèle.",{},{"id":345,"data":346,"type":240,"tunes":348},"rrlavifFLP",{"text":347},"La compression, la normalisation, le découpage en segments et la synthèse sont souvent nécessaires. L'objectif est de préserver les propriétés requises pour une interprétation correcte, et non d'éliminer la transformation.",{},{"id":350,"data":351,"type":240,"tunes":353},"CM_pAZ8ozk",{"text":352},"La question d'ingénierie difficile est donc : quelles propriétés sont sémantiquement porteuses ?",{},{"id":355,"data":356,"type":42,"tunes":358},"U8VCT8z_Nf",{"text":357,"level":234},"Réponse directe",{},{"id":360,"data":361,"type":240,"tunes":363},"Hs1rUljfaq",{"text":362},"Une preuve survit à un pipeline d'IA lorsque les transformations entre la source et le modèle préservent suffisamment d'identité, de provenance, de structure, d'unités, de conditions, de conflits et de métadonnées de validité pour que l'information récupérée signifie la même chose en contexte qu'à la source.",{},{"id":365,"data":366,"type":286,"tunes":368},"2r2HxbzQFg",{"code":367},"Evidence Preservation\n=\nmeaning\n+ identity\n+ provenance\n+ scope\n+ units\n+ conditions\n+ validity\n+ traceability",{},{"id":370,"data":371,"type":42,"tunes":373},"ka_HV02MYw",{"text":372,"level":234},"Pourquoi le RAG peut échouer même avec la bonne source",{},{"id":375,"data":376,"type":240,"tunes":378},"eKv9aiSIC9",{"text":377},"Un récupérateur peut trouver le bon document tout en fournissant la mauvaise unité de preuve.",{},{"id":380,"data":381,"type":240,"tunes":383},"-EAX8UkdxQ",{"text":382},"Le problème peut être survenu bien plus tôt : lors de l'OCR, de l'analyse de documents, de l'extraction HTML, de la conversion de tableaux, du mappage de champs, de la construction de segments ou de la perte de métadonnées.",{},{"id":385,"data":386,"type":393,"tunes":394},"oYCdWaadX3",{"link":387,"meta":388},"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fwhere-does-an-llm-get-its-data-rag-data-sources-in-python",{"image":389,"title":391,"description":392},{"url":390},"","Où un LLM obtient-il ses données ? Sources de données RAG en Python","Le RAG commence par des sources de données externes et des méthodes de récupération. La préservation des preuves demande ce qui doit survivre entre la source originale et le contexte récupéré.","linkTool",{},{"id":396,"data":397,"type":42,"tunes":399},"A_6idqO75O",{"text":398,"level":234},"Les huit points de préservation",{},{"id":401,"data":402,"type":42,"tunes":404},"NaU6o_qWBJ",{"text":403,"level":264},"1. Identité",{},{"id":406,"data":407,"type":240,"tunes":409},"5mR31I7W1M",{"text":408},"Le pipeline doit préserver quelle entité, produit, document, utilisateur, contrat ou enregistrement la preuve décrit réellement.",{},{"id":411,"data":412,"type":42,"tunes":414},"UIaEdeVESf",{"text":413,"level":264},"2. Provenance",{},{"id":416,"data":417,"type":240,"tunes":419},"vAtWlAr_L5",{"text":418},"Le système devrait pouvoir retracer la preuve récupérée jusqu'à sa source originale plutôt que seulement jusqu'à un identifiant de segment anonyme.",{},{"id":421,"data":422,"type":42,"tunes":424},"6EGnXzdt_j",{"text":423,"level":264},"3. Structure",{},{"id":426,"data":427,"type":240,"tunes":429},"9NSFX7Dq4k",{"text":428},"Les tableaux, les titres, les listes, les relations parent-enfant et les exceptions peuvent porter une signification qui disparaît lorsque tout est aplati en fragments de texte indépendants.",{},{"id":431,"data":432,"type":42,"tunes":434},"87QZ9m4_q7",{"text":433,"level":264},"4. Unités",{},{"id":436,"data":437,"type":240,"tunes":439},"0e7PrlT5MU",{"text":438},"Un nombre sans son unité peut devenir inutile ou dangereux. 100 peut signifier millisecondes, kilogrammes, euros, volts ou pourcentage d'une référence.",{},{"id":441,"data":442,"type":42,"tunes":444},"Bid-hGyRbP",{"text":443,"level":264},"5. Conditions et exceptions",{},{"id":446,"data":447,"type":240,"tunes":449},"Td_5jAm0ME",{"text":448},"Les règles contiennent fréquemment des conditions d'applicabilité. Séparer la règle de l'exception peut préserver les mots tout en détruisant le sens.",{},{"id":451,"data":452,"type":42,"tunes":454},"y5CedEdzCc",{"text":453,"level":264},"6. Version et temps",{},{"id":456,"data":457,"type":240,"tunes":459},"K9nDZI5yQf",{"text":458},"Les preuves doivent préserver le moment où elles sont devenues valides, le moment où elles ont été observées et si un objet plus récent les remplace.",{},{"id":461,"data":462,"type":42,"tunes":464},"UtnbpFacpC",{"text":463,"level":264},"7. État de conflit",{},{"id":466,"data":467,"type":240,"tunes":469},"rQMwxCKxIE",{"text":468},"Si deux sources sont en désaccord, la normalisation ne doit pas sélectionner silencieusement une valeur et effacer le fait que le désaccord existait.",{},{"id":471,"data":472,"type":42,"tunes":474},"615Imi6TRP",{"text":473,"level":264},"8. Portée d'accès",{},{"id":476,"data":477,"type":240,"tunes":479},"WZuieLPrus",{"text":478},"Les limites de permission et de locataire doivent rester attachées aux preuves tout au long de la récupération et de l'assemblage du contexte.",{},{"id":481,"data":482,"type":42,"tunes":484},"je6wCH54vs",{"text":483,"level":234},"Des données brutes aux preuves contrôlées",{},{"id":486,"data":487,"type":240,"tunes":489},"fqi7FxVIcZ",{"text":488},"L'endroit le plus solide pour préserver les preuves est souvent avant la vectorisation.",{},{"id":491,"data":492,"type":240,"tunes":494},"iA9_KWjiwC",{"text":493},"Si l'identité de la source, la validité et les conflits sont perdus avant la création des embeddings, la récupération ne peut pas les reconstruire magiquement plus tard.",{},{"id":496,"data":497,"type":393,"tunes":503},"y6cwyGR2PK",{"link":498,"meta":499},"https:\u002F\u002Fstajic.de\u002Ffr\u002Fportfolio\u002Ffrom-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information",{"image":500,"title":501,"description":502},{"url":390},"Des données brutes au contexte IA vérifiable : une architecture pratique pour une information prête pour les agents","L'architecture commence avant la récupération en préservant l'identité, la provenance, les conflits, les inconnues et la validité dans des objets d'information réutilisables.",{},{"id":505,"data":506,"type":42,"tunes":508},"piEK0zmvxT",{"text":507,"level":234},"L'ingénierie du contexte fait partie de la chaîne",{},{"id":510,"data":511,"type":240,"tunes":513},"Hs-hKf-7cy",{"text":512},"Même des preuves correctement récupérées peuvent être endommagées lors de l'assemblage du contexte.",{},{"id":515,"data":516,"type":240,"tunes":518},"kdWLXPTg4h",{"text":517},"Un constructeur de contexte peut tronquer une exception, supprimer les étiquettes de source, fusionner des passages contradictoires, réordonner les dépendances ou dépasser le budget d'attention utile du modèle.",{},{"id":520,"data":521,"type":393,"tunes":527},"vQ5Jya2Rv8",{"link":522,"meta":523},"https:\u002F\u002Fstajic.de\u002Ffr\u002Fblog\u002Fwhat-is-context-engineering-what-the-model-receives-before-it-answers",{"image":524,"title":525,"description":526},{"url":390},"Qu'est-ce que l'ingénierie de contexte ? Ce que le modèle reçoit avant de répondre","L'ingénierie de contexte détermine quelles informations récupérées parviennent réellement au modèle et sous quelles contraintes structurelles, de validité et de sécurité.",{},{"id":529,"data":530,"type":42,"tunes":532},"oC18nhSD3Y",{"text":531,"level":234},"Lien avec la distance probatoire",{},{"id":534,"data":535,"type":240,"tunes":537},"tbewzxhkkL",{"text":536},"La préservation des preuves et la distance probatoire décrivent différents modes de défaillance.",{},{"id":539,"data":540,"type":286,"tunes":542},"XzYMjN1nNf",{"code":541},"Evidence Preservation Chain:\nDid the evidence survive transformation?\n\nEvidence Distance:\nHow far is the final claim from that evidence?",{},{"id":544,"data":545,"type":240,"tunes":547},"013RZ4lijY",{"text":546},"Un système peut préserver parfaitement les preuves et néanmoins en tirer une inférence non étayée. Inversement, le raisonnement final peut être simple alors que les preuves elles-mêmes étaient déjà corrompues plus tôt dans le pipeline.",{},{"id":549,"data":550,"type":42,"tunes":552},"cdXu2rD4d-",{"text":551,"level":234},"Lien avec la couche d'autorité de récupération",{},{"id":554,"data":555,"type":240,"tunes":557},"asRwQ_zGye",{"text":556},"La couche d'autorité de récupération sélectionne la source qui mérite un poids probatoire.",{},{"id":559,"data":560,"type":240,"tunes":562},"dMwV7G7LC4",{"text":561},"La chaîne de préservation des preuves garantit que les propriétés conférant son autorité à cette source ne sont pas écartées après la sélection.",{},{"id":564,"data":565,"type":286,"tunes":567},"ky0NtD4IAl",{"code":566},"Authority at source\n        ↓\nPreservation through pipeline\n        ↓\nAuthority still visible in context",{},{"id":569,"data":570,"type":42,"tunes":572},"Em0D8AL_b7",{"text":571,"level":234},"Modèle de données pratique",{},{"id":574,"data":575,"type":240,"tunes":577},"iDWVeCI3US",{"text":576},"Une unité de récupération peut transporter plus que du texte.",{},{"id":579,"data":580,"type":286,"tunes":582},"Ybc3rCbvY0",{"code":581},"{\n  \"text\": \"Maximum operating temperature is 40 °C...\",\n  \"sourceId\": \"manual-v4.2\",\n  \"entityId\": \"device-184\",\n  \"section\": \"Operating limits\",\n  \"validFrom\": \"2026-03-01\",\n  \"unit\": \"°C\",\n  \"conditions\": [\"35 °C when accessory X is installed\"],\n  \"authority\": \"manufacturer_manual\",\n  \"conflicts\": [],\n  \"accessScope\": \"public\"\n}",{},{"id":584,"data":585,"type":240,"tunes":587},"g007D00k4V",{"text":586},"Le schéma exact dépend de l'application. Le principe architectural est que le texte récupérable ne doit pas se détacher des métadonnées nécessaires à son interprétation.",{},{"id":589,"data":590,"type":42,"tunes":592},"Wi-cP9HpKU",{"text":591,"level":234},"Méthode de diagnostic \u002F de décision",{},{"id":594,"data":595,"type":240,"tunes":597},"yUPTwfB9uP",{"text":596},"Choisissez une réponse importante et retracez une affirmation justificative à rebours dans le système.",{},{"id":599,"data":600,"type":611,"tunes":612},"IRrMfVVINa",{"meta":601,"items":603,"style":610},{"counterType":602},"numeric",[604,605,606,607,608,609],"Identifier l'affirmation finale.","Identifier le passage de contexte exact qui l'a étayée.","Identifier le fragment récupéré qui a produit ce passage.","Identifier l'enregistrement normalisé ou le document analysé derrière le fragment.","Identifier la source originale.","Comparer le sens, la portée, les unités, les conditions et la version à chaque transition.","ordered","list",{},{"id":614,"data":615,"type":240,"tunes":617},"05LsNWXcjf",{"text":616},"Tout point où un sens essentiel disparaît constitue une rupture dans la chaîne de préservation des preuves.",{},{"id":619,"data":620,"type":42,"tunes":622},"raBFHAylDf",{"text":621,"level":234},"Mode de défaillance : le fragment orphelin",{},{"id":624,"data":625,"type":240,"tunes":627},"iniLj3zXLh",{"text":626},"Un fragment orphelin contient un texte d'apparence utile mais sans relation fiable avec la structure du document, l'identité de la source, la version ou les conditions d'applicabilité dont il provient.",{},{"id":629,"data":630,"type":286,"tunes":632},"1zagZ01tls",{"code":631},"\"Refunds are available for 30 days.\"\n\nMissing:\nWhich product?\nWhich country?\nWhich policy version?\nFrom what effective date?\nWhat exceptions?",{},{"id":634,"data":635,"type":240,"tunes":637},"sZ0-pxHB2K",{"text":636},"La phrase peut être authentique et rester une preuve non fiable.",{},{"id":639,"data":640,"type":42,"tunes":642},"wCo24MdrUE",{"text":641,"level":234},"Mode de défaillance : Normalisation par invention",{},{"id":644,"data":645,"type":240,"tunes":647},"_8tEy420r3",{"text":646},"La normalisation devrait aligner les représentations. Elle ne devrait pas inventer silencieusement des valeurs manquantes pour donner aux enregistrements une apparence complète.",{},{"id":649,"data":650,"type":286,"tunes":652},"DvCye0QKKX",{"code":651},"Unknown\n≠\nFalse\n≠\nZero\n≠\nDefault",{},{"id":654,"data":655,"type":240,"tunes":657},"gTRNnqfBbR",{"text":656},"Préserver les inconnues fait partie de la préservation des preuves.",{},{"id":659,"data":660,"type":42,"tunes":662},"Azp57iMou6",{"text":661,"level":234},"Mode de défaillance : Effondrement des conflits",{},{"id":664,"data":665,"type":240,"tunes":667},"HXmXzrSkqL",{"text":666},"Supposons qu'une source approuvée indique 24 mois et qu'une autre indique 36 mois.",{},{"id":669,"data":670,"type":240,"tunes":672},"qDsZWYHjhc",{"text":671},"Un pipeline qui stocke simplement la dernière valeur importée produit une base de données propre et détruit la preuve qu'un conflit existait.",{},{"id":674,"data":675,"type":240,"tunes":677},"NkYagndI9y",{"text":676},"Un système vérifiable devrait préserver les deux valeurs, leur provenance et le conflit non résolu jusqu'à ce qu'une règle ou une décision humaine le résolve.",{},{"id":679,"data":680,"type":42,"tunes":682},"KrRJTl84Dq",{"text":681,"level":234},"Mode de défaillance : Compression du contexte",{},{"id":684,"data":685,"type":240,"tunes":687},"VVy7pkhXMG",{"text":686},"La compression du contexte peut être utile, mais un résumé qui supprime les qualificatifs peut accroître la certitude apparente.",{},{"id":689,"data":690,"type":286,"tunes":692},"MTqe1-sAHt",{"code":691},"Source:\n\"Evidence suggests X under conditions A and B, but the sample is small.\"\n\nBad compression:\n\"X is proven.\"",{},{"id":694,"data":695,"type":240,"tunes":697},"d61VdY7Z5d",{"text":696},"L'information est devenue plus courte et moins véridique en même temps.",{},{"id":699,"data":700,"type":42,"tunes":702},"k4jHwZaoLl",{"text":701,"level":234},"Cas limite : La compression avec perte peut rester correcte",{},{"id":704,"data":705,"type":240,"tunes":707},"92uTDyGplP",{"text":706},"Toutes les pertes ne sont pas nuisibles.",{},{"id":709,"data":710,"type":240,"tunes":712},"U0cu-bjBmq",{"text":711},"Supprimer la mise en forme visuelle d'un paragraphe peut n'avoir aucune conséquence sémantique. Convertir des degrés Celsius en kelvins peut préserver le sens si la transformation est explicite et correcte. Dédupliquer des copies exactes peut améliorer la récupération sans perdre de preuves.",{},{"id":714,"data":715,"type":240,"tunes":717},"GJpi4mOxtH",{"text":716},"L'objectif n'est donc pas un stockage sans perte à chaque étape. C'est une préservation sémantiquement suffisante.",{},{"id":719,"data":720,"type":42,"tunes":722},"7XXF7EHC5m",{"text":721,"level":234},"Limites",{},{"id":724,"data":725,"type":240,"tunes":727},"FW8TcQldR-",{"text":726},"La chaîne de préservation des preuves est un cadre architectural, et non une garantie que les preuves préservées seront interprétées correctement.",{},{"id":729,"data":730,"type":240,"tunes":732},"V1rP2gB1C8",{"text":731},"Un modèle peut recevoir des preuves parfaitement préservées et mal raisonner malgré tout. Un système de récupération peut préserver la provenance et classer néanmoins la mauvaise source. Une source elle-même peut aussi être erronée.",{},{"id":734,"data":735,"type":240,"tunes":737},"GCfe1Di90D",{"text":736},"La préservation protège le pipeline de preuves. Elle ne remplace pas l'évaluation des sources ni le raisonnement.",{},{"id":739,"data":740,"type":42,"tunes":742},"654Bx39EsI",{"text":741,"level":234},"Qu'est-ce qui changerait cette réponse ?",{},{"id":744,"data":745,"type":240,"tunes":747},"UFx-k4mGz6",{"text":746},"Les systèmes futurs pourront récupérer des objets de preuve structurés plutôt que des fragments de texte, préserver automatiquement les graphes de provenance et exposer directement aux modèles et aux agents des contraintes de validité lisibles par machine.",{},{"id":749,"data":750,"type":240,"tunes":752},"lQMoO7JEMG",{"text":751},"Cela rendrait la préservation des preuves plus facile à mettre en œuvre, mais rendrait le principe plus important plutôt que moins important.",{},{"id":754,"data":755,"type":42,"tunes":757},"gL1c_bEYfC",{"text":756,"level":234},"Conclusion",{},{"id":759,"data":760,"type":240,"tunes":762},"xElsMHA7R-",{"text":761},"Une IA fiable nécessite plus que de trouver une source correcte.",{},{"id":764,"data":765,"type":240,"tunes":767},"Heg2LJJSzg",{"text":766},"Les propriétés qui rendent la source utile doivent survivre à l'analyse syntaxique, à la normalisation, au découpage, à la récupération et à l'assemblage du contexte.",{},{"id":769,"data":770,"type":286,"tunes":772},"MzylWKf1Jm",{"code":771},"Source Authority\n      ↓\nEvidence Preservation Chain\n      ↓\nRetrievable Evidence\n      ↓\nEvidence Distance\n      ↓\nReasoning\n      ↓\nAnswer Validity Boundary",{},{"id":774,"data":775,"type":240,"tunes":777},"m_RMMC7dVx",{"text":776},"Si la chaîne se rompt, le modèle peut recevoir des informations techniquement authentiques mais qui ne sont plus sûres à interpréter comme les preuves d'origine.",{},{"id":779,"data":780,"type":42,"tunes":782},"IkNMvsnAJq",{"text":781,"level":234},"Sources primaires",{},{"id":784,"data":785,"type":240,"tunes":787},"QqTaKgzQ8k",{"text":786},"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).",{},{"id":789,"data":790,"type":240,"tunes":792},"UturEzvxw2",{"text":791},"Nelson F. Liu et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.03172\" target=\"_blank\">Lost in the Middle: How Language Models Use Long Contexts\u003C\u002Fa> (2023).",{},{"id":794,"data":795,"type":240,"tunes":797},"I2fpdupo7S",{"text":796},"W3C, \u003Ca href=\"https:\u002F\u002Fwww.w3.org\u002FTR\u002Fprov-overview\u002F\" target=\"_blank\">PROV Overview\u003C\u002Fa>.",{},"2.31","Une preuve peut être correcte à la source et devenir néanmoins peu fiable après l'analyse syntaxique, la normalisation, le découpage, la récupération et l'assemblage du contexte. La chaîne de préservation des preuves vérifie si l'identité, la provenance, la portée et le sens survivent à l'ensemble du pipeline d'IA.","\u002Fuploads\u002F2026\u002F10\u002Fcan-evidence-survive-the-ai-pipeline-the-evidence-preservation-chain-1792096396174-urdg9z.webp","can-evidence-survive-the-ai-pipeline-the-evidence-preservation-chain-1792096396174-urdg9z","PUBLISHED","2026-10-15T16:31:00.000Z","2026-10-15T20:31:59.026Z","2026-10-15T20:38:15.936Z",{"en":807,"de":808,"sr":809,"es":810,"fr":811,"it":812,"ru":813,"zh":814},"\u002Fblog\u002Fcan-evidence-survive-the-ai-pipeline-the-evidence-preservation-chain","\u002Fde\u002Fblog\u002Fcan-evidence-survive-the-ai-pipeline-the-evidence-preservation-chain","\u002Fsr\u002Fblog\u002Fcan-evidence-survive-the-ai-pipeline-the-evidence-preservation-chain","\u002Fes\u002Fblog\u002Fcan-evidence-survive-the-ai-pipeline-the-evidence-preservation-chain","\u002Ffr\u002Fblog\u002Fcan-evidence-survive-the-ai-pipeline-the-evidence-preservation-chain","\u002Fit\u002Fblog\u002Fcan-evidence-survive-the-ai-pipeline-the-evidence-preservation-chain","\u002Fru\u002Fblog\u002Fcan-evidence-survive-the-ai-pipeline-the-evidence-preservation-chain","\u002Fzh\u002Fblog\u002Fcan-evidence-survive-the-ai-pipeline-the-evidence-preservation-chain",[816,820],{"id":817,"name":818,"slug":819},68,"Risques, contrôles et preuves","risks-and-controls",{"id":821,"name":822,"slug":823},49,"Contrôles et preuves","controls",{"id":825,"login":826,"email":390,"displayName":827},"20","rooth8233","Aleksandar Stajić",[829,1269],{"lang":830,"title":831,"content":832,"contentJson":833,"excerpt":1268},"en","Can Evidence Survive the AI Pipeline? — The Evidence Preservation Chain","{\"time\":1792096415735,\"blocks\":[{\"id\":\"tP10jlMV2G\",\"type\":\"header\",\"data\":{\"text\":\"Question\",\"level\":2},\"tunes\":{}},{\"id\":\"zD8a-5x7r9\",\"type\":\"paragraph\",\"data\":{\"text\":\"What happens when the original source is correct, but the AI pipeline strips away the information required to understand that source correctly?\"},\"tunes\":{}},{\"id\":\"LJjTjSV74R\",\"type\":\"paragraph\",\"data\":{\"text\":\"A reliable document can become unreliable context if parsing removes its table structure, chunking separates a condition from its exception, normalization drops units, retrieval loses version metadata or context assembly hides a conflict.\"},\"tunes\":{}},{\"id\":\"kOzPNiKvzu\",\"type\":\"paragraph\",\"data\":{\"text\":\"This article uses the term Evidence Preservation Chain for the requirement that evidence retain the properties necessary for correct interpretation as it moves from source to final AI context.\"},\"tunes\":{}},{\"id\":\"xUf9Qt8UHn\",\"type\":\"quote\",\"data\":{\"text\":\"An Evidence Preservation Chain is the end-to-end sequence of transformations through which evidence must retain identity, provenance, scope, meaning and validity before it can safely support an AI-generated claim.\",\"caption\":\"Working definition — an architectural concept introduced here, not a standardized research term.\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"RxQsaJsNo6\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"NBMzNZ_qHB\",\"type\":\"header\",\"data\":{\"text\":\"What This Really Means\",\"level\":2},\"tunes\":{}},{\"id\":\"zAEKGXCEmK\",\"type\":\"paragraph\",\"data\":{\"text\":\"Most discussions about RAG focus on whether relevant information can be found.\"},\"tunes\":{}},{\"id\":\"i2gu8IM3j1\",\"type\":\"paragraph\",\"data\":{\"text\":\"But evidence usually passes through several transformations before the model sees it.\"},\"tunes\":{}},{\"id\":\"lN52dh3dxK\",\"type\":\"code\",\"data\":{\"code\":\"Original Source\\n   ↓\\nParsing\\n   ↓\\nNormalization\\n   ↓\\nChunking\\n   ↓\\nIndexing\\n   ↓\\nRetrieval\\n   ↓\\nReranking\\n   ↓\\nContext Assembly\\n   ↓\\nModel\\n   ↓\\nClaim\"},\"tunes\":{}},{\"id\":\"G90TdnX4WC\",\"type\":\"paragraph\",\"data\":{\"text\":\"The evidence can degrade at any point in that chain even when the original document was perfectly correct.\"},\"tunes\":{}},{\"id\":\"mC_OsF2ji3\",\"type\":\"header\",\"data\":{\"text\":\"Simplest Example\",\"level\":2},\"tunes\":{}},{\"id\":\"P8XUivVa7g\",\"type\":\"paragraph\",\"data\":{\"text\":\"Imagine a product manual contains this statement:\"},\"tunes\":{}},{\"id\":\"2LamDXESHK\",\"type\":\"quote\",\"data\":{\"text\":\"Maximum operating temperature: 40 °C, except when accessory X is installed, in which case the limit is 35 °C.\",\"caption\":\"Example source statement\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"BNY1TVLg3y\",\"type\":\"paragraph\",\"data\":{\"text\":\"A poor chunking process splits the sentence.\"},\"tunes\":{}},{\"id\":\"xaAZ_aJazc\",\"type\":\"code\",\"data\":{\"code\":\"Chunk A:\\nMaximum operating temperature: 40 °C.\\n\\nChunk B:\\nExcept when accessory X is installed, in which case the limit is 35 °C.\"},\"tunes\":{}},{\"id\":\"q0ORvmk__K\",\"type\":\"paragraph\",\"data\":{\"text\":\"The retriever returns only Chunk A.\"},\"tunes\":{}},{\"id\":\"MupQI-_xIz\",\"type\":\"paragraph\",\"data\":{\"text\":\"The retrieved text is not fabricated. It came directly from the manual. Yet the context is incomplete in exactly the way that matters.\"},\"tunes\":{}},{\"id\":\"cYEvuH7MR6\",\"type\":\"code\",\"data\":{\"code\":\"Correct source\\n        +\\nlossy transformation\\n        =\\nmisleading evidence\"},\"tunes\":{}},{\"id\":\"F_ZvT1AfTN\",\"type\":\"header\",\"data\":{\"text\":\"Where the Example Stops Working\",\"level\":2},\"tunes\":{}},{\"id\":\"flP5_wVcvt\",\"type\":\"paragraph\",\"data\":{\"text\":\"Evidence preservation does not mean copying every source byte unchanged into the model context.\"},\"tunes\":{}},{\"id\":\"rrlavifFLP\",\"type\":\"paragraph\",\"data\":{\"text\":\"Compression, normalization, chunking and summarization are often necessary. The goal is to preserve the properties required for correct interpretation, not to eliminate transformation.\"},\"tunes\":{}},{\"id\":\"CM_pAZ8ozk\",\"type\":\"paragraph\",\"data\":{\"text\":\"The difficult engineering question is therefore: Which properties are semantically load-bearing?\"},\"tunes\":{}},{\"id\":\"U8VCT8z_Nf\",\"type\":\"header\",\"data\":{\"text\":\"Direct Answer\",\"level\":2},\"tunes\":{}},{\"id\":\"Hs1rUljfaq\",\"type\":\"paragraph\",\"data\":{\"text\":\"Evidence survives an AI pipeline when the transformations between source and model preserve enough identity, provenance, structure, units, conditions, conflicts and validity metadata for the retrieved information to mean the same thing in context that it meant at the source.\"},\"tunes\":{}},{\"id\":\"2r2HxbzQFg\",\"type\":\"code\",\"data\":{\"code\":\"Evidence Preservation\\n=\\nmeaning\\n+ identity\\n+ provenance\\n+ scope\\n+ units\\n+ conditions\\n+ validity\\n+ traceability\"},\"tunes\":{}},{\"id\":\"ka_HV02MYw\",\"type\":\"header\",\"data\":{\"text\":\"Why RAG Can Fail Even With the Right Source\",\"level\":2},\"tunes\":{}},{\"id\":\"eKv9aiSIC9\",\"type\":\"paragraph\",\"data\":{\"text\":\"A retriever can find the correct document while still supplying the wrong evidence unit.\"},\"tunes\":{}},{\"id\":\"-EAX8UkdxQ\",\"type\":\"paragraph\",\"data\":{\"text\":\"The problem may have occurred much earlier: during OCR, document parsing, HTML extraction, table conversion, field mapping, chunk construction or metadata loss.\"},\"tunes\":{}},{\"id\":\"oYCdWaadX3\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhere-does-an-llm-get-its-data-rag-data-sources-in-python\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Where Does an LLM Get Its Data? RAG Data Sources in Python\",\"description\":\"RAG begins with external data sources and retrieval methods. Evidence preservation asks what must survive between the original source and the retrieved context.\"}},\"tunes\":{}},{\"id\":\"A_6idqO75O\",\"type\":\"header\",\"data\":{\"text\":\"The Eight Preservation Points\",\"level\":2},\"tunes\":{}},{\"id\":\"NaU6o_qWBJ\",\"type\":\"header\",\"data\":{\"text\":\"1. Identity\",\"level\":3},\"tunes\":{}},{\"id\":\"5mR31I7W1M\",\"type\":\"paragraph\",\"data\":{\"text\":\"The pipeline must preserve what entity, product, document, user, contract or record the evidence actually describes.\"},\"tunes\":{}},{\"id\":\"UIaEdeVESf\",\"type\":\"header\",\"data\":{\"text\":\"2. Provenance\",\"level\":3},\"tunes\":{}},{\"id\":\"vAtWlAr_L5\",\"type\":\"paragraph\",\"data\":{\"text\":\"The system should be able to trace the retrieved evidence back to its original source rather than only to an anonymous chunk identifier.\"},\"tunes\":{}},{\"id\":\"6EGnXzdt_j\",\"type\":\"header\",\"data\":{\"text\":\"3. Structure\",\"level\":3},\"tunes\":{}},{\"id\":\"9NSFX7Dq4k\",\"type\":\"paragraph\",\"data\":{\"text\":\"Tables, headings, lists, parent-child relationships and exceptions can carry meaning that disappears when everything is flattened into independent text fragments.\"},\"tunes\":{}},{\"id\":\"87QZ9m4_q7\",\"type\":\"header\",\"data\":{\"text\":\"4. Units\",\"level\":3},\"tunes\":{}},{\"id\":\"0e7PrlT5MU\",\"type\":\"paragraph\",\"data\":{\"text\":\"A number without its unit can become useless or dangerous. 100 may mean milliseconds, kilograms, euros, volts or percent of a baseline.\"},\"tunes\":{}},{\"id\":\"Bid-hGyRbP\",\"type\":\"header\",\"data\":{\"text\":\"5. Conditions and Exceptions\",\"level\":3},\"tunes\":{}},{\"id\":\"Td_5jAm0ME\",\"type\":\"paragraph\",\"data\":{\"text\":\"Rules frequently contain applicability conditions. Separating the rule from the exception can preserve the words while destroying the meaning.\"},\"tunes\":{}},{\"id\":\"y5CedEdzCc\",\"type\":\"header\",\"data\":{\"text\":\"6. Version and Time\",\"level\":3},\"tunes\":{}},{\"id\":\"K9nDZI5yQf\",\"type\":\"paragraph\",\"data\":{\"text\":\"Evidence should preserve when it became valid, when it was observed and whether a newer object supersedes it.\"},\"tunes\":{}},{\"id\":\"UtnbpFacpC\",\"type\":\"header\",\"data\":{\"text\":\"7. Conflict State\",\"level\":3},\"tunes\":{}},{\"id\":\"rQMwxCKxIE\",\"type\":\"paragraph\",\"data\":{\"text\":\"If two sources disagree, normalization should not silently select one value and erase the fact that disagreement existed.\"},\"tunes\":{}},{\"id\":\"615Imi6TRP\",\"type\":\"header\",\"data\":{\"text\":\"8. Access Scope\",\"level\":3},\"tunes\":{}},{\"id\":\"WZuieLPrus\",\"type\":\"paragraph\",\"data\":{\"text\":\"Permission and tenant boundaries must remain attached to evidence throughout retrieval and context assembly.\"},\"tunes\":{}},{\"id\":\"je6wCH54vs\",\"type\":\"header\",\"data\":{\"text\":\"From Raw Data to Controlled Evidence\",\"level\":2},\"tunes\":{}},{\"id\":\"fqi7FxVIcZ\",\"type\":\"paragraph\",\"data\":{\"text\":\"The strongest place to preserve evidence is often before vectorization.\"},\"tunes\":{}},{\"id\":\"iA9_KWjiwC\",\"type\":\"paragraph\",\"data\":{\"text\":\"If source identity, validity and conflicts are lost before embeddings are created, retrieval cannot magically reconstruct them later.\"},\"tunes\":{}},{\"id\":\"y6cwyGR2PK\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fstajic.de\u002Fportfolio\u002Ffrom-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"From Raw Data to Verifiable AI Context: A Practical Architecture for Agent-Ready Information\",\"description\":\"The architecture starts before retrieval by preserving identity, provenance, conflicts, unknowns and validity in reusable information objects.\"}},\"tunes\":{}},{\"id\":\"piEK0zmvxT\",\"type\":\"header\",\"data\":{\"text\":\"Context Engineering Is Part of the Chain\",\"level\":2},\"tunes\":{}},{\"id\":\"Hs-hKf-7cy\",\"type\":\"paragraph\",\"data\":{\"text\":\"Even correctly retrieved evidence can be damaged during context assembly.\"},\"tunes\":{}},{\"id\":\"kdWLXPTg4h\",\"type\":\"paragraph\",\"data\":{\"text\":\"A context builder may truncate an exception, remove source labels, merge conflicting passages, reorder dependencies or exceed the model's useful attention budget.\"},\"tunes\":{}},{\"id\":\"vQ5Jya2Rv8\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhat-is-context-engineering-what-the-model-receives-before-it-answers\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"What Is Context Engineering? What the Model Receives Before It Answers\",\"description\":\"Context engineering determines which retrieved information actually reaches the model and under which structural, validity and security constraints.\"}},\"tunes\":{}},{\"id\":\"oC18nhSD3Y\",\"type\":\"header\",\"data\":{\"text\":\"Connection to Evidence Distance\",\"level\":2},\"tunes\":{}},{\"id\":\"tbewzxhkkL\",\"type\":\"paragraph\",\"data\":{\"text\":\"Evidence Preservation and Evidence Distance describe different failure modes.\"},\"tunes\":{}},{\"id\":\"XzYMjN1nNf\",\"type\":\"code\",\"data\":{\"code\":\"Evidence Preservation Chain:\\nDid the evidence survive transformation?\\n\\nEvidence Distance:\\nHow far is the final claim from that evidence?\"},\"tunes\":{}},{\"id\":\"013RZ4lijY\",\"type\":\"paragraph\",\"data\":{\"text\":\"A system can preserve evidence perfectly and still make an unsupported inference from it. Conversely, the final reasoning can be simple while the evidence itself was already corrupted earlier in the pipeline.\"},\"tunes\":{}},{\"id\":\"cdXu2rD4d-\",\"type\":\"header\",\"data\":{\"text\":\"Connection to the Retrieval Authority Layer\",\"level\":2},\"tunes\":{}},{\"id\":\"asRwQ_zGye\",\"type\":\"paragraph\",\"data\":{\"text\":\"The Retrieval Authority Layer selects which source deserves evidential weight.\"},\"tunes\":{}},{\"id\":\"dMwV7G7LC4\",\"type\":\"paragraph\",\"data\":{\"text\":\"The Evidence Preservation Chain ensures that the properties making that source authoritative are not discarded after selection.\"},\"tunes\":{}},{\"id\":\"ky0NtD4IAl\",\"type\":\"code\",\"data\":{\"code\":\"Authority at source\\n        ↓\\nPreservation through pipeline\\n        ↓\\nAuthority still visible in context\"},\"tunes\":{}},{\"id\":\"Em0D8AL_b7\",\"type\":\"header\",\"data\":{\"text\":\"Practical Data Model\",\"level\":2},\"tunes\":{}},{\"id\":\"iDWVeCI3US\",\"type\":\"paragraph\",\"data\":{\"text\":\"A retrieval unit can carry more than text.\"},\"tunes\":{}},{\"id\":\"Ybc3rCbvY0\",\"type\":\"code\",\"data\":{\"code\":\"{\\n  \\\"text\\\": \\\"Maximum operating temperature is 40 °C...\\\",\\n  \\\"sourceId\\\": \\\"manual-v4.2\\\",\\n  \\\"entityId\\\": \\\"device-184\\\",\\n  \\\"section\\\": \\\"Operating limits\\\",\\n  \\\"validFrom\\\": \\\"2026-03-01\\\",\\n  \\\"unit\\\": \\\"°C\\\",\\n  \\\"conditions\\\": [\\\"35 °C when accessory X is installed\\\"],\\n  \\\"authority\\\": \\\"manufacturer_manual\\\",\\n  \\\"conflicts\\\": [],\\n  \\\"accessScope\\\": \\\"public\\\"\\n}\"},\"tunes\":{}},{\"id\":\"g007D00k4V\",\"type\":\"paragraph\",\"data\":{\"text\":\"The exact schema depends on the application. The architectural principle is that retrievable text should not become detached from the metadata required to interpret it.\"},\"tunes\":{}},{\"id\":\"Wi-cP9HpKU\",\"type\":\"header\",\"data\":{\"text\":\"Diagnostic \u002F Decision Method\",\"level\":2},\"tunes\":{}},{\"id\":\"yUPTwfB9uP\",\"type\":\"paragraph\",\"data\":{\"text\":\"Choose one important answer and trace one supporting claim backwards through the system.\"},\"tunes\":{}},{\"id\":\"IRrMfVVINa\",\"type\":\"list\",\"data\":{\"style\":\"ordered\",\"meta\":{\"counterType\":\"numeric\"},\"items\":[\"Identify the final claim.\",\"Identify the exact context passage that supported it.\",\"Identify the retrieved chunk that produced that passage.\",\"Identify the normalized record or parsed document behind the chunk.\",\"Identify the original source.\",\"Compare meaning, scope, units, conditions and version at every transition.\"]},\"tunes\":{}},{\"id\":\"05LsNWXcjf\",\"type\":\"paragraph\",\"data\":{\"text\":\"Any point where essential meaning disappears is a break in the Evidence Preservation Chain.\"},\"tunes\":{}},{\"id\":\"raBFHAylDf\",\"type\":\"header\",\"data\":{\"text\":\"Failure Mode: The Orphan Chunk\",\"level\":2},\"tunes\":{}},{\"id\":\"iniLj3zXLh\",\"type\":\"paragraph\",\"data\":{\"text\":\"An orphan chunk contains useful-looking text but no reliable relationship to the document structure, source identity, version or applicability conditions from which it came.\"},\"tunes\":{}},{\"id\":\"1zagZ01tls\",\"type\":\"code\",\"data\":{\"code\":\"\\\"Refunds are available for 30 days.\\\"\\n\\nMissing:\\nWhich product?\\nWhich country?\\nWhich policy version?\\nFrom what effective date?\\nWhat exceptions?\"},\"tunes\":{}},{\"id\":\"sZ0-pxHB2K\",\"type\":\"paragraph\",\"data\":{\"text\":\"The sentence may be authentic and still be unsafe evidence.\"},\"tunes\":{}},{\"id\":\"wCo24MdrUE\",\"type\":\"header\",\"data\":{\"text\":\"Failure Mode: Normalization by Invention\",\"level\":2},\"tunes\":{}},{\"id\":\"_8tEy420r3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Normalization should align representations. It should not silently invent missing values to make records look complete.\"},\"tunes\":{}},{\"id\":\"DvCye0QKKX\",\"type\":\"code\",\"data\":{\"code\":\"Unknown\\n≠\\nFalse\\n≠\\nZero\\n≠\\nDefault\"},\"tunes\":{}},{\"id\":\"gTRNnqfBbR\",\"type\":\"paragraph\",\"data\":{\"text\":\"Preserving unknowns is part of preserving evidence.\"},\"tunes\":{}},{\"id\":\"Azp57iMou6\",\"type\":\"header\",\"data\":{\"text\":\"Failure Mode: Conflict Collapse\",\"level\":2},\"tunes\":{}},{\"id\":\"HXmXzrSkqL\",\"type\":\"paragraph\",\"data\":{\"text\":\"Suppose one approved source says 24 months and another says 36 months.\"},\"tunes\":{}},{\"id\":\"qDsZWYHjhc\",\"type\":\"paragraph\",\"data\":{\"text\":\"A pipeline that simply stores the last imported value produces a clean database and destroys the evidence that a conflict existed.\"},\"tunes\":{}},{\"id\":\"NkYagndI9y\",\"type\":\"paragraph\",\"data\":{\"text\":\"A verifiable system should preserve both values, their provenance and the unresolved conflict until a rule or human decision resolves it.\"},\"tunes\":{}},{\"id\":\"KrRJTl84Dq\",\"type\":\"header\",\"data\":{\"text\":\"Failure Mode: Context Compression\",\"level\":2},\"tunes\":{}},{\"id\":\"VVy7pkhXMG\",\"type\":\"paragraph\",\"data\":{\"text\":\"Context compression can be useful, but a summary that removes qualifiers can increase apparent certainty.\"},\"tunes\":{}},{\"id\":\"MTqe1-sAHt\",\"type\":\"code\",\"data\":{\"code\":\"Source:\\n\\\"Evidence suggests X under conditions A and B, but the sample is small.\\\"\\n\\nBad compression:\\n\\\"X is proven.\\\"\"},\"tunes\":{}},{\"id\":\"d61VdY7Z5d\",\"type\":\"paragraph\",\"data\":{\"text\":\"The information became shorter and less truthful at the same time.\"},\"tunes\":{}},{\"id\":\"k4jHwZaoLl\",\"type\":\"header\",\"data\":{\"text\":\"Edge Case: Lossy Compression Can Still Be Correct\",\"level\":2},\"tunes\":{}},{\"id\":\"92uTDyGplP\",\"type\":\"paragraph\",\"data\":{\"text\":\"Not every loss is harmful.\"},\"tunes\":{}},{\"id\":\"U0cu-bjBmq\",\"type\":\"paragraph\",\"data\":{\"text\":\"Removing visual formatting from a paragraph may have no semantic consequence. Converting Celsius to Kelvin can preserve meaning if the transformation is explicit and correct. Deduplicating exact copies can improve retrieval without losing evidence.\"},\"tunes\":{}},{\"id\":\"GJpi4mOxtH\",\"type\":\"paragraph\",\"data\":{\"text\":\"The objective is therefore not lossless storage at every stage. It is semantically sufficient preservation.\"},\"tunes\":{}},{\"id\":\"7XXF7EHC5m\",\"type\":\"header\",\"data\":{\"text\":\"Limitations\",\"level\":2},\"tunes\":{}},{\"id\":\"FW8TcQldR-\",\"type\":\"paragraph\",\"data\":{\"text\":\"The Evidence Preservation Chain is an architectural framework, not a guarantee that preserved evidence will be interpreted correctly.\"},\"tunes\":{}},{\"id\":\"V1rP2gB1C8\",\"type\":\"paragraph\",\"data\":{\"text\":\"A model can receive perfectly preserved evidence and still reason badly. A retriever can preserve provenance and still rank the wrong source. A source itself can also be wrong.\"},\"tunes\":{}},{\"id\":\"GCfe1Di90D\",\"type\":\"paragraph\",\"data\":{\"text\":\"Preservation protects the evidence pipeline. It does not replace source evaluation or reasoning.\"},\"tunes\":{}},{\"id\":\"654Bx39EsI\",\"type\":\"header\",\"data\":{\"text\":\"What Would Change This Answer?\",\"level\":2},\"tunes\":{}},{\"id\":\"UFx-k4mGz6\",\"type\":\"paragraph\",\"data\":{\"text\":\"Future systems may retrieve structured evidence objects rather than text chunks, preserve provenance graphs automatically and expose machine-readable validity constraints directly to models and agents.\"},\"tunes\":{}},{\"id\":\"lQMoO7JEMG\",\"type\":\"paragraph\",\"data\":{\"text\":\"That would make evidence preservation easier to implement, but it would make the principle more important rather than less important.\"},\"tunes\":{}},{\"id\":\"gL1c_bEYfC\",\"type\":\"header\",\"data\":{\"text\":\"Conclusion\",\"level\":2},\"tunes\":{}},{\"id\":\"xElsMHA7R-\",\"type\":\"paragraph\",\"data\":{\"text\":\"Reliable AI requires more than finding a correct source.\"},\"tunes\":{}},{\"id\":\"Heg2LJJSzg\",\"type\":\"paragraph\",\"data\":{\"text\":\"The properties that make the source useful must survive parsing, normalization, chunking, retrieval and context assembly.\"},\"tunes\":{}},{\"id\":\"MzylWKf1Jm\",\"type\":\"code\",\"data\":{\"code\":\"Source Authority\\n      ↓\\nEvidence Preservation Chain\\n      ↓\\nRetrievable Evidence\\n      ↓\\nEvidence Distance\\n      ↓\\nReasoning\\n      ↓\\nAnswer Validity Boundary\"},\"tunes\":{}},{\"id\":\"m_RMMC7dVx\",\"type\":\"paragraph\",\"data\":{\"text\":\"If the chain breaks, the model may receive information that is technically authentic but no longer safe to interpret as the original evidence.\"},\"tunes\":{}},{\"id\":\"IkNMvsnAJq\",\"type\":\"header\",\"data\":{\"text\":\"Primary Sources\",\"level\":2},\"tunes\":{}},{\"id\":\"QqTaKgzQ8k\",\"type\":\"paragraph\",\"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).\"},\"tunes\":{}},{\"id\":\"UturEzvxw2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Nelson F. Liu et al., \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.03172\\\" target=\\\"_blank\\\">Lost in the Middle: How Language Models Use Long Contexts\u003C\u002Fa> (2023).\"},\"tunes\":{}},{\"id\":\"I2fpdupo7S\",\"type\":\"paragraph\",\"data\":{\"text\":\"W3C, \u003Ca href=\\\"https:\u002F\u002Fwww.w3.org\u002FTR\u002Fprov-overview\u002F\\\" target=\\\"_blank\\\">PROV Overview\u003C\u002Fa>.\"},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":834,"blocks":835,"version":1267},1792096415735,[836,839,843,847,851,856,860,864,868,872,875,879,883,887,892,896,899,903,907,910,914,918,922,926,930,934,937,941,945,949,957,961,965,969,972,976,979,983,987,991,995,999,1003,1007,1011,1015,1019,1023,1027,1031,1035,1043,1047,1051,1055,1063,1067,1071,1074,1078,1082,1086,1090,1093,1097,1101,1104,1108,1112,1116,1127,1131,1135,1139,1142,1146,1150,1154,1157,1161,1165,1169,1173,1177,1181,1185,1188,1192,1196,1200,1204,1208,1212,1216,1220,1224,1228,1232,1236,1239,1243,1247,1250,1254,1258,1261,1264],{"id":231,"data":837,"type":42,"tunes":838},{"text":233,"level":234},{},{"id":237,"data":840,"type":240,"tunes":842},{"text":841},"What happens when the original source is correct, but the AI pipeline strips away the information required to understand that source correctly?",{},{"id":243,"data":844,"type":240,"tunes":846},{"text":845},"A reliable document can become unreliable context if parsing removes its table structure, chunking separates a condition from its exception, normalization drops units, retrieval loses version metadata or context assembly hides a conflict.",{},{"id":248,"data":848,"type":240,"tunes":850},{"text":849},"This article uses the term Evidence Preservation Chain for the requirement that evidence retain the properties necessary for correct interpretation as it moves from source to final AI context.",{},{"id":253,"data":852,"type":258,"tunes":855},{"text":853,"caption":854,"alignment":257},"An Evidence Preservation Chain is the end-to-end sequence of transformations through which evidence must retain identity, provenance, scope, meaning and validity before it can safely support an AI-generated claim.","Working definition — an architectural concept introduced here, not a standardized research term.",{},{"id":261,"data":857,"type":265,"tunes":859},{"title":858,"maxLevel":264,"minLevel":234},"Contents",{},{"id":268,"data":861,"type":42,"tunes":863},{"text":862,"level":234},"What This Really Means",{},{"id":273,"data":865,"type":240,"tunes":867},{"text":866},"Most discussions about RAG focus on whether relevant information can be found.",{},{"id":278,"data":869,"type":240,"tunes":871},{"text":870},"But evidence usually passes through several transformations before the model sees it.",{},{"id":283,"data":873,"type":286,"tunes":874},{"code":285},{},{"id":289,"data":876,"type":240,"tunes":878},{"text":877},"The evidence can degrade at any point in that chain even when the original document was perfectly correct.",{},{"id":294,"data":880,"type":42,"tunes":882},{"text":881,"level":234},"Simplest Example",{},{"id":299,"data":884,"type":240,"tunes":886},{"text":885},"Imagine a product manual contains this statement:",{},{"id":304,"data":888,"type":258,"tunes":891},{"text":889,"caption":890,"alignment":257},"Maximum operating temperature: 40 °C, except when accessory X is installed, in which case the limit is 35 °C.","Example source statement",{},{"id":310,"data":893,"type":240,"tunes":895},{"text":894},"A poor chunking process splits the sentence.",{},{"id":315,"data":897,"type":286,"tunes":898},{"code":317},{},{"id":320,"data":900,"type":240,"tunes":902},{"text":901},"The retriever returns only Chunk A.",{},{"id":325,"data":904,"type":240,"tunes":906},{"text":905},"The retrieved text is not fabricated. It came directly from the manual. Yet the context is incomplete in exactly the way that matters.",{},{"id":330,"data":908,"type":286,"tunes":909},{"code":332},{},{"id":335,"data":911,"type":42,"tunes":913},{"text":912,"level":234},"Where the Example Stops Working",{},{"id":340,"data":915,"type":240,"tunes":917},{"text":916},"Evidence preservation does not mean copying every source byte unchanged into the model context.",{},{"id":345,"data":919,"type":240,"tunes":921},{"text":920},"Compression, normalization, chunking and summarization are often necessary. The goal is to preserve the properties required for correct interpretation, not to eliminate transformation.",{},{"id":350,"data":923,"type":240,"tunes":925},{"text":924},"The difficult engineering question is therefore: Which properties are semantically load-bearing?",{},{"id":355,"data":927,"type":42,"tunes":929},{"text":928,"level":234},"Direct Answer",{},{"id":360,"data":931,"type":240,"tunes":933},{"text":932},"Evidence survives an AI pipeline when the transformations between source and model preserve enough identity, provenance, structure, units, conditions, conflicts and validity metadata for the retrieved information to mean the same thing in context that it meant at the source.",{},{"id":365,"data":935,"type":286,"tunes":936},{"code":367},{},{"id":370,"data":938,"type":42,"tunes":940},{"text":939,"level":234},"Why RAG Can Fail Even With the Right Source",{},{"id":375,"data":942,"type":240,"tunes":944},{"text":943},"A retriever can find the correct document while still supplying the wrong evidence unit.",{},{"id":380,"data":946,"type":240,"tunes":948},{"text":947},"The problem may have occurred much earlier: during OCR, document parsing, HTML extraction, table conversion, field mapping, chunk construction or metadata loss.",{},{"id":385,"data":950,"type":393,"tunes":956},{"link":951,"meta":952},"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhere-does-an-llm-get-its-data-rag-data-sources-in-python",{"image":953,"title":954,"description":955},{"url":390},"Where Does an LLM Get Its Data? RAG Data Sources in Python","RAG begins with external data sources and retrieval methods. Evidence preservation asks what must survive between the original source and the retrieved context.",{},{"id":396,"data":958,"type":42,"tunes":960},{"text":959,"level":234},"The Eight Preservation Points",{},{"id":401,"data":962,"type":42,"tunes":964},{"text":963,"level":264},"1. Identity",{},{"id":406,"data":966,"type":240,"tunes":968},{"text":967},"The pipeline must preserve what entity, product, document, user, contract or record the evidence actually describes.",{},{"id":411,"data":970,"type":42,"tunes":971},{"text":413,"level":264},{},{"id":416,"data":973,"type":240,"tunes":975},{"text":974},"The system should be able to trace the retrieved evidence back to its original source rather than only to an anonymous chunk identifier.",{},{"id":421,"data":977,"type":42,"tunes":978},{"text":423,"level":264},{},{"id":426,"data":980,"type":240,"tunes":982},{"text":981},"Tables, headings, lists, parent-child relationships and exceptions can carry meaning that disappears when everything is flattened into independent text fragments.",{},{"id":431,"data":984,"type":42,"tunes":986},{"text":985,"level":264},"4. Units",{},{"id":436,"data":988,"type":240,"tunes":990},{"text":989},"A number without its unit can become useless or dangerous. 100 may mean milliseconds, kilograms, euros, volts or percent of a baseline.",{},{"id":441,"data":992,"type":42,"tunes":994},{"text":993,"level":264},"5. Conditions and Exceptions",{},{"id":446,"data":996,"type":240,"tunes":998},{"text":997},"Rules frequently contain applicability conditions. Separating the rule from the exception can preserve the words while destroying the meaning.",{},{"id":451,"data":1000,"type":42,"tunes":1002},{"text":1001,"level":264},"6. Version and Time",{},{"id":456,"data":1004,"type":240,"tunes":1006},{"text":1005},"Evidence should preserve when it became valid, when it was observed and whether a newer object supersedes it.",{},{"id":461,"data":1008,"type":42,"tunes":1010},{"text":1009,"level":264},"7. Conflict State",{},{"id":466,"data":1012,"type":240,"tunes":1014},{"text":1013},"If two sources disagree, normalization should not silently select one value and erase the fact that disagreement existed.",{},{"id":471,"data":1016,"type":42,"tunes":1018},{"text":1017,"level":264},"8. Access Scope",{},{"id":476,"data":1020,"type":240,"tunes":1022},{"text":1021},"Permission and tenant boundaries must remain attached to evidence throughout retrieval and context assembly.",{},{"id":481,"data":1024,"type":42,"tunes":1026},{"text":1025,"level":234},"From Raw Data to Controlled Evidence",{},{"id":486,"data":1028,"type":240,"tunes":1030},{"text":1029},"The strongest place to preserve evidence is often before vectorization.",{},{"id":491,"data":1032,"type":240,"tunes":1034},{"text":1033},"If source identity, validity and conflicts are lost before embeddings are created, retrieval cannot magically reconstruct them later.",{},{"id":496,"data":1036,"type":393,"tunes":1042},{"link":1037,"meta":1038},"https:\u002F\u002Fstajic.de\u002Fportfolio\u002Ffrom-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information",{"image":1039,"title":1040,"description":1041},{"url":390},"From Raw Data to Verifiable AI Context: A Practical Architecture for Agent-Ready Information","The architecture starts before retrieval by preserving identity, provenance, conflicts, unknowns and validity in reusable information objects.",{},{"id":505,"data":1044,"type":42,"tunes":1046},{"text":1045,"level":234},"Context Engineering Is Part of the Chain",{},{"id":510,"data":1048,"type":240,"tunes":1050},{"text":1049},"Even correctly retrieved evidence can be damaged during context assembly.",{},{"id":515,"data":1052,"type":240,"tunes":1054},{"text":1053},"A context builder may truncate an exception, remove source labels, merge conflicting passages, reorder dependencies or exceed the model's useful attention budget.",{},{"id":520,"data":1056,"type":393,"tunes":1062},{"link":1057,"meta":1058},"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhat-is-context-engineering-what-the-model-receives-before-it-answers",{"image":1059,"title":1060,"description":1061},{"url":390},"What Is Context Engineering? What the Model Receives Before It Answers","Context engineering determines which retrieved information actually reaches the model and under which structural, validity and security constraints.",{},{"id":529,"data":1064,"type":42,"tunes":1066},{"text":1065,"level":234},"Connection to Evidence Distance",{},{"id":534,"data":1068,"type":240,"tunes":1070},{"text":1069},"Evidence Preservation and Evidence Distance describe different failure modes.",{},{"id":539,"data":1072,"type":286,"tunes":1073},{"code":541},{},{"id":544,"data":1075,"type":240,"tunes":1077},{"text":1076},"A system can preserve evidence perfectly and still make an unsupported inference from it. Conversely, the final reasoning can be simple while the evidence itself was already corrupted earlier in the pipeline.",{},{"id":549,"data":1079,"type":42,"tunes":1081},{"text":1080,"level":234},"Connection to the Retrieval Authority Layer",{},{"id":554,"data":1083,"type":240,"tunes":1085},{"text":1084},"The Retrieval Authority Layer selects which source deserves evidential weight.",{},{"id":559,"data":1087,"type":240,"tunes":1089},{"text":1088},"The Evidence Preservation Chain ensures that the properties making that source authoritative are not discarded after selection.",{},{"id":564,"data":1091,"type":286,"tunes":1092},{"code":566},{},{"id":569,"data":1094,"type":42,"tunes":1096},{"text":1095,"level":234},"Practical Data Model",{},{"id":574,"data":1098,"type":240,"tunes":1100},{"text":1099},"A retrieval unit can carry more than text.",{},{"id":579,"data":1102,"type":286,"tunes":1103},{"code":581},{},{"id":584,"data":1105,"type":240,"tunes":1107},{"text":1106},"The exact schema depends on the application. The architectural principle is that retrievable text should not become detached from the metadata required to interpret it.",{},{"id":589,"data":1109,"type":42,"tunes":1111},{"text":1110,"level":234},"Diagnostic \u002F Decision Method",{},{"id":594,"data":1113,"type":240,"tunes":1115},{"text":1114},"Choose one important answer and trace one supporting claim backwards through the system.",{},{"id":599,"data":1117,"type":611,"tunes":1126},{"meta":1118,"items":1119,"style":610},{"counterType":602},[1120,1121,1122,1123,1124,1125],"Identify the final claim.","Identify the exact context passage that supported it.","Identify the retrieved chunk that produced that passage.","Identify the normalized record or parsed document behind the chunk.","Identify the original source.","Compare meaning, scope, units, conditions and version at every transition.",{},{"id":614,"data":1128,"type":240,"tunes":1130},{"text":1129},"Any point where essential meaning disappears is a break in the Evidence Preservation Chain.",{},{"id":619,"data":1132,"type":42,"tunes":1134},{"text":1133,"level":234},"Failure Mode: The Orphan Chunk",{},{"id":624,"data":1136,"type":240,"tunes":1138},{"text":1137},"An orphan chunk contains useful-looking text but no reliable relationship to the document structure, source identity, version or applicability conditions from which it came.",{},{"id":629,"data":1140,"type":286,"tunes":1141},{"code":631},{},{"id":634,"data":1143,"type":240,"tunes":1145},{"text":1144},"The sentence may be authentic and still be unsafe evidence.",{},{"id":639,"data":1147,"type":42,"tunes":1149},{"text":1148,"level":234},"Failure Mode: Normalization by Invention",{},{"id":644,"data":1151,"type":240,"tunes":1153},{"text":1152},"Normalization should align representations. It should not silently invent missing values to make records look complete.",{},{"id":649,"data":1155,"type":286,"tunes":1156},{"code":651},{},{"id":654,"data":1158,"type":240,"tunes":1160},{"text":1159},"Preserving unknowns is part of preserving evidence.",{},{"id":659,"data":1162,"type":42,"tunes":1164},{"text":1163,"level":234},"Failure Mode: Conflict Collapse",{},{"id":664,"data":1166,"type":240,"tunes":1168},{"text":1167},"Suppose one approved source says 24 months and another says 36 months.",{},{"id":669,"data":1170,"type":240,"tunes":1172},{"text":1171},"A pipeline that simply stores the last imported value produces a clean database and destroys the evidence that a conflict existed.",{},{"id":674,"data":1174,"type":240,"tunes":1176},{"text":1175},"A verifiable system should preserve both values, their provenance and the unresolved conflict until a rule or human decision resolves it.",{},{"id":679,"data":1178,"type":42,"tunes":1180},{"text":1179,"level":234},"Failure Mode: Context Compression",{},{"id":684,"data":1182,"type":240,"tunes":1184},{"text":1183},"Context compression can be useful, but a summary that removes qualifiers can increase apparent certainty.",{},{"id":689,"data":1186,"type":286,"tunes":1187},{"code":691},{},{"id":694,"data":1189,"type":240,"tunes":1191},{"text":1190},"The information became shorter and less truthful at the same time.",{},{"id":699,"data":1193,"type":42,"tunes":1195},{"text":1194,"level":234},"Edge Case: Lossy Compression Can Still Be Correct",{},{"id":704,"data":1197,"type":240,"tunes":1199},{"text":1198},"Not every loss is harmful.",{},{"id":709,"data":1201,"type":240,"tunes":1203},{"text":1202},"Removing visual formatting from a paragraph may have no semantic consequence. 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