Retrieval provides candidate evidence, but it is not proof. The article outlines a practical design for systems that convert model statements into inspectable claims, enforce a publication gate, and maintain traceable evidence bundles.
Treat retrieved documents as candidates, not proof: every material claim needs a verifiable evidence link and a recorded support decision.
Replace document-level control with a claim ledger: record atomic claims, exact text spans, verbatim evidence spans, source snapshots, and metadata for traceability.
Enforce a non-negotiable publication gate: material claims without acceptable support must be revised, abstained, labeled as inference, or escalated to review.
It can conflict with another source, be untrusted, or even maliciously injected. It's a wonderful technology and extremely useful – however, I'm arguing against treating retrieval as an oracle of truth. The original RAG paper showed why retrieval is valuable for knowledge intensive generation, and it also identified provenance as an open problem.
They determine what our precision, recall, coverage and abstention metrics will mean. I would start with a versioned, held-out test case: the request, the evidence snapshot, the expected claims, the support policy, permitted abstention behaviour and a risk tier. The post-RAG engineering objective thus has to be stronger than "RAG with citations." The real objective is an evidence-grounded narrative system.
Retrieval gives a model access to candidates for evidence. It doesn't prove that a retrieved passage supports a claim it just made. A retrieved passage can be stale or incomplete.
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A framework for building RAG pipelines that introduces complexity in response to observed failure modes, from lexical and hybrid search to reranking and agentic information seeking The post Why RAG Complexity Should Be Earned appeared first on Towards Data Science .
Retrieval-augmented generation solved a real problem: it grounded LLM outputs in facts the model was never trained on. But classic RAG has a ceiling. It retrieves once, stuffs the results into a prompt, and hopes the top-k chunks happen to contain the answer. There's no self-correction, no multi-step reasoning, and no
RAG retrieves. Agents act. I built both separately, connected them explicitly, and ran the same nine tasks through all three systems. The post RAG Isn't an Agent — I Built the Layer Between Retrieval and Action appeared first on Towards Data Science .
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Over the past several years, I have worked through three successive generations of intelligent retrieval systems, each solving problems the previous generation could not. Here is what I have learned.
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