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      "relation": "enables"
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          "json": "https://banes-lab.com/json/records/lexicon/evidence-citation",
          "label": "Evidence Citation",
          "markdown": "https://banes-lab.com/records/lexicon/evidence-citation.md",
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          "markdown": "https://banes-lab.com/records/vocabulary/severity-contextual.md",
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          "json": "https://banes-lab.com/json/records/architecture/vector-search",
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          "markdown": "https://banes-lab.com/ontology/principles/architecture-category-model-architecture.md",
          "ref": "chapter:/ontology#architecture-category-model-architecture"
        },
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          "href": "https://banes-lab.com/ontology/lexicon#lexicon-category-model-architecture",
          "json": "https://banes-lab.com/json/ontology/lexicon/lexicon-category-model-architecture",
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          "ref": "chapter:/ontology/lexicon#lexicon-category-model-architecture"
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          "markdown": "https://banes-lab.com/ontology/schema/the-vocabulary-severity.md",
          "ref": "chapter:/ontology/schema#the-vocabulary-severity"
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      "relation": "linked-from"
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  ],
  "summary": "A design pattern that retrieves relevant documents for a question and passes them to the model, so the answer can cite its sources.",
  "siblings": {
    "next": {
      "href": "https://banes-lab.com/ontology#architecture-vector-search",
      "json": "https://banes-lab.com/json/records/architecture/vector-search",
      "label": "Vector Search",
      "markdown": "https://banes-lab.com/records/architecture/vector-search.md",
      "ref": "architecture:vector-search"
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      "ref": "architecture:model-inference"
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    "json": "https://banes-lab.com/json/api/records/architecture",
    "label": "Architecture principles",
    "markdown": "https://banes-lab.com/api/records/architecture.md",
    "ref": "api:records/architecture"
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  "aliases": [
    "RAG"
  ],
  "exemplar": {
    "after": "const query = normalizeFooQuery(question);\nconst documents = await fooRetriever.search(query, { topK: 8 });\nconst groundedPrompt = buildGroundedFooPrompt(question, documents);\nconst answer = await model.generate(groundedPrompt);\nreturn attachCitations(answer, documents);",
    "before": "const answer = await model.generate(`Answer: ${question}`);",
    "lang": "ts",
    "medium": "code"
  },
  "formedBy": null,
  "scope": [
    "language-model system",
    "knowledge retrieval"
  ],
  "severity": "contextual"
}