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In an enterprise RAG architecture, how does non-parametric grounding fundamentally alter the operational role of the generative language model?

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beginner 45 min

Day 1: Frame the Question RAG Must Answer

Frame the Question RAG Must Answer

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In the FieldDesk pre-retrieval query boundary validator above, why must the pipeline return PROMPT_USER when equipment_model is absent instead of running an unconstrained vector search?

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In PolicyDesk, an employee in Munich queries statutory parental leave allowances. The retrieval engine searches the entire global corpus, retrieves the top 20 chunks by cosine similarity, and then applies post-retrieval filtering in application memory to remove non-German documents. The user receives an empty result because all 20 retrieved chunks were US and UK policies, even though active German policies exist at ranks 24 through 28. What architectural defect occurred, and what is the proper fix?

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