A governed retrieval system that answers employee questions from internal docs — with permissions, citations, and an eval harness that catches drift.
Every large company has the same problem: the answer exists, but it is buried across a wiki, a dozen Slack channels, a SharePoint nobody trusts, and three people''s heads. A naive "chat with your docs" demo is easy; an enterprise-grade internal RAG system is not, because it has to respect document-level permissions, cite its sources, handle conflicting versions, and not hallucinate policy. The systems that survive contact with auditors share four traits: permission-aware retrieval (a user only retrieves what they can already see), mandatory citations, an explicit "I don''t know" path, and a continuous eval harness. Build the eval harness first — it is what separates a system you can trust from a demo you cannot.
Catalogue every knowledge source, its owner, freshness, and sensitivity. Decide what is in scope — and explicitly exclude what is stale or unowned.
Index documents with their access-control metadata so retrieval filters to what the asking user is already entitled to see. Permissions are a retrieval-time filter, not an afterthought.
Every answer links the exact passages it used. No citation, no answer — the model returns 'not found' rather than guessing.
Curate a golden set of 100-300 real questions with verified answers. Score retrieval recall and answer faithfulness on every change, in CI.
Launch to one team, capture thumbs-down with reasons, fix the underlying docs or retrieval, then expand. Treat bad answers as documentation bugs.
Use these templates as-is or customize for your business.
{"question":"...","expected_answer":"...","must_cite":["doc-id-1"],"acceptable_to_say_unknown":false,"owner":"team"}For each generated answer: extract every claim, verify each is supported by a cited passage. Faithfulness = supported claims / total claims. Block deploys that drop below 0.95 on the golden set.
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Retrieval-augmented generation: the agent answers strictly from a curated corpus of your documents and history. Cheaper, more controllable, and fewer hallucinations than open-ended generation.
Learn the agentic glossary →Where this workflow tends to break in production — and what to put in place before you ship it.
Permission leak — user retrieves a document they should not see
Mitigation: Enforce access control at retrieval time from the source system; never rely on the prompt to withhold content. Audit with red-team queries.
Hallucinated policy when retrieval finds nothing
Mitigation: Hard-require citations; an answer with no supporting passage returns 'not found' and offers to route to a human owner.
Silent quality drift as docs change
Mitigation: Run the golden eval set in CI on every index rebuild and model change; alert on faithfulness regressions.
Skip this if your underlying documentation is wrong or contradictory — RAG will faithfully retrieve the wrong answer. It is a retrieval system, not a fact-checker. Fix document ownership and freshness first.
A phased approach to get this workflow running and delivering ROI.
Days 1–30
Foundation
Days 31–60
Optimization
Days 61–90
Scale
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