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    "story_id": "mp-2026-10-24-007",
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    "headline": "Regulated Claims Move Their Rules Into the Data Model",
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    "dek": "A deployed pharmaceutical tool reports stronger audience, content and quality targeting after compliance context moved from prompts into structured retrieval.",
    "summary": "A deployed pharmaceutical tool reports stronger audience, content and quality targeting after compliance context moved from prompts into structured retrieval.",
    "body_text": "A production case study compares prompt-level rules with a Golden Data Model covering 1,867 pharmaceutical claims across four products. The structured retrieval layer attaches references, audience labels, material type, usage history, linked text variants and a six-dimension quality score before generation. Across six product-audience segments, the authors report improvements in intended indication, content type, target audience, quality score and claim reuse, alongside a 29.5 percent reduction in weekly API cost. The paper labels this descriptive deployment evidence; it does not establish clinical efficacy or regulatory approval.",
    "why_it_matters": "A deployed pharmaceutical tool reports stronger audience, content and quality targeting after compliance context moved from prompts into structured retrieval.",
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      "The paper labels this descriptive deployment evidence; it does not establish clinical efficacy or regulatory approval."
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    "tags": [
      "regulated content",
      "retrieval",
      "data architecture"
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  "sources": [
    {
      "source_id": "source-2026-10-24-007",
      "title": "arXiv preprint 2610.31519, original v1",
      "publisher": "arXiv",
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      "published_at": "2026-10-22T12:31:36.000-04:00",
      "accessed_at": "2026-10-24T08:23:48.747-04:00",
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    "name": "The Machine Press",
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    "description": "A daily newspaper for the age of artificial intelligence."
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    "title": "Regulated Claims Move Their Rules Into the Data Model",
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