RAG Evaluation Set TemplateProduction AI partner

RAG evaluation set template for private document search.

A useful RAG evaluation set is not just a list of questions. It is a structured review table that names the expected source, answer shape, access rules, refusal cases, and reviewer decision criteria for each example.

ChecklistEvaluationHandoff

Checklist

Evaluation

Handoff

Reader fit

Built for teams turning AI ideas into production decisions.

Operations, healthcare, finance, support, legal, and internal tools teams preparing RAG over private documents.

Each test case should include the question, expected source, answer shape, access rule, and acceptance criteria.

Include positive answers, stale-source traps, missing-context cases, permission failures, and escalation examples.

Reviewer notes should decide whether to tune retrieval, chunking, prompts, permissions, or the workflow itself.

Guide

The practical checks.

Template fields to include

Use one row per evaluation case. Include the user question, user role, expected source document, expected section, acceptable answer shape, required citation, access rule, and expected behavior.

Add columns for retrieval result, answer quality, citation accuracy, refusal behavior, escalation behavior, reviewer notes, and the release decision.

Positive and negative cases

A healthy set includes questions the system should answer and questions it should not answer. Positive cases test whether the right source is found. Negative cases test missing context, stale documents, restricted records, and conflicting policies.

For private document search, refusal and escalation examples are as important as successful answers because employees may act on the output.

Permission and source ownership

Every case should name the user role and whether that role is allowed to retrieve the source. If the answer would expose restricted content, the expected behavior should be refusal or escalation without leaking source details.

Source ownership should be explicit. A policy owner, analyst, clinical operator, or department lead should be able to update the expected answer when the underlying document changes.

Review cadence and release criteria

Review failures weekly during the pilot. Separate retrieval misses, citation mistakes, permission issues, weak answers, and questions that reveal a workflow gap.

Before rollout, define the minimum acceptable score for source retrieval, citation accuracy, refusal behavior, and reviewer acceptance. The threshold should match the risk of the workflow.

Checklist

Use this before you scope the first build.

Create one row per real user question or workflow example.

Label user role, expected source, expected section, and answer shape.

Mark each case as answer, refuse, escalate, or ask for clarification.

Score retrieval, citation, answer quality, permission behavior, and reviewer acceptance separately.

Assign a source owner who can update expected answers when documents change.

Use failed cases to decide whether to tune retrieval, permissions, prompts, or scope.

Related services

Service paths for this guide.

RAG Development

Give your team fast, source-backed answers across policies, records, filings, and internal documents.

AI Consulting

Choose the right workflow, define the business result, and move from AI idea to production without a long strategy phase.

Custom AI Models

Turn proprietary examples and domain knowledge into a production capability without overspending on model training.

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.

SEC Filing Monitoring AI

Give analysts timely filing alerts, concise change summaries, and direct links to the source text.

Private Equity Diligence AI Agent

Give deal teams faster first-pass briefs, source trails, risk flags, and follow-up questions from approved materials.

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.