Moonveil AI Inc.

RAG Evaluation Checklist

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.

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.

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.

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Track failure modes such as outdated documents, near-duplicate policies, access-restricted sources, and chunks that split important context.

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.

Related services

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RAG DevelopmentCustom AI Models

Related use cases

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Internal Knowledge Base RAGHealthcare Policy and SOP RAGMedical Record Summarization AI

Moonveil AI Inc.

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