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Collect representative user questions and expected source material.
RAG Evaluation ChecklistProduction AI partner
RAG evaluation checklist for internal knowledge bases.
RAG systems look good in demos when documents are clean and questions are simple. Production use is harder: users ask messy questions, source material conflicts, permissions matter, and a confident answer can still be wrong.
ChecklistEvaluationHandoff
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Checklist
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Evaluation
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Handoff
Reader fit
Built for teams turning AI ideas into production decisions.
Teams building RAG over policies, SOPs, contracts, tickets, records, research notes, or technical documentation.
Test retrieval and answer quality separately.
Use source-backed questions that reflect real employee workflows.
Treat permissions, missing context, and refusal behavior as first-class requirements.
Guide
The practical checks.
01
Build an evaluation set from real questions
Start with questions employees already ask in Slack, support queues, onboarding, compliance reviews, or analyst workflows. Include easy lookups, multi-document questions, ambiguous requests, and questions the system should refuse.
For every question, keep the expected source document, the expected answer shape, and the reason a reviewer would accept or reject the answer.
02
Measure retrieval before generation
If the right source is missing, the final answer cannot be trusted. Evaluate whether the retriever finds the correct document, section, table, or record before judging the generated response.
Track failure modes such as outdated documents, near-duplicate policies, access-restricted sources, and chunks that split important context.
03
Make uncertainty visible
A useful internal knowledge assistant should say when it cannot answer from approved sources. It should show citations, explain missing context, and route the user to the next best source or owner.
This is especially important for healthcare, finance, legal, and operational workflows where users may act on the answer.
Checklist
Use this before you scope the first build.
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Score retrieval quality separately from final answer quality.
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Check citation accuracy, not just citation presence.
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Test permission boundaries with users who have different access levels.
05
Add examples where the correct behavior is to refuse or escalate.
06
Review logs for repeated missing-source and stale-document failures.
Related use cases
Use cases this guide supports.
Internal Knowledge Base RAG
Give employees fast, citation-backed answers across policies, SOPs, contracts, records, and internal documents.
Healthcare Policy and SOP RAG
Give staff fast, citation-backed answers across policies, SOPs, protocols, payer rules, and guidance.
Medical Record Summarization AI
Give reviewers concise, source-backed summaries of long records and documents without hiding uncertainty.
Moonveil AI
Want this turned into a production-ready agent?
Moonveil can apply the checklist and take one workflow from scope to launch in 4–8 weeks.