Retrieval-Augmented Generation for Internal Docs: A Small-Team Evaluation Runbook

A retrieval-augmented generation (RAG) system can search approved documents and ask a language model to answer from retrieved passages, but retrieval quality and permissions determine usefulness. Start with a narrow corpus, preserve document ownership and dates, show citations, and test “I don’t know” behavior. Measure retrieval recall, groundedness, freshness, latency, and unauthorized leakage before expanding.

A practical workflow

1. Inventory authoritative documents, owners, versions, and access groups.
2. Chunk documents without destroying headings, tables, warnings, or citations.
3. Create a question set containing answerable, ambiguous, outdated, and unanswerable prompts.
4. Evaluate retrieved passages separately from generated answers.
5. Add deletion, re-indexing, permission, and incident procedures.

Before you submit, buy, or publish

• Never bypass source permissions in the index.
• Expose document titles and passages so users can verify answers.
• Treat generated summaries as untrusted until checked.
• Version prompts, models, embedding settings, and evaluation results.

Caveats that matter

RAG does not eliminate hallucinations, copyright, privacy, or records-management risks. Model and vendor behavior changes require regression testing.

Frequently asked question

Does adding more documents always improve RAG? No. Duplicates, stale pages, and poor chunking can make answers worse.

Primary source

Use the official source for current eligibility, forms, dates, fees, limits, and policy changes: Open the primary source. Verify the page immediately before acting; this guide is educational and is not legal, tax, immigration, or financial advice.