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The RAG Pattern That Stops LLMs From Inventing Fields That Don't Exist

AK-RAG is a reference architecture that indexes enterprise attribute metadata as first-class knowledge objects, then routes LLM output through a six-step governed pipeline — parse, retrieve, classify, clarify, govern, emit DSL — so the LLM can only select attributes that exist in your catalog, never invent them.

Hi there,

Every team building RAG for banking, healthcare, or insurance eventually hits the same wall: the LLM retrieves relevant context but then generates field names, thresholds, and attribute identifiers that don't exist in the actual data model. In regulated industries that isn't a nuisance — it is a governance failure. AK-RAG is a reference architecture that fixes this at the retrieval unit level, not at the prompt level.

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The RAG Pattern That Stops LLMs From Inventing Fields That Don't Exist

  • AK-RAG indexes your governed attribute catalog — not documents — so every retrieval unit is a real, validated enterprise field
  • Six-step pipeline: parse phrases → hybrid BM25 + kNN retrieval → exact/near/ambiguous/none decision → clarify → governance check → emit DSL
  • The LLM only selects from attribute_ids that exist; it cannot invent fields, thresholds, or identifiers
  • Applies unchanged to banking (credit utilization, KYC, AML attributes), healthcare (HbA1c, HEDIS measures), insurance, and any enterprise metadata catalog
  • Open source reference implementation at github.com/crazyaiml/attribute-knowledge-rag

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— Bhanu @ superml.dev