Production RAG Evaluation: Accuracy, Permissions & Citations

Who this is for

You have a RAG system in production or approaching launch. Users report wrong answers or missing information, and you have no systematic way to measure or improve quality.

Common situations

• No evaluation dataset—quality is measured by user complaints only
• Retrieval returns irrelevant chunks that the LLM confidently synthesizes into wrong answers
• Citations point to documents the user cannot access or that no longer exist
• Permission boundaries are not tested—cross-tenant or cross-role leakage possible
• Stale documents remain indexed after updates or deletions

What we deliver

• Evaluation dataset design and seeding methodology
• Retrieval precision and recall measurement
• Answer faithfulness and citation verification approaches
• Permission boundary test suite design
• Content freshness monitoring and alerting

Use cases

• RAG quality monitoring in production
• Pre-launch RAG system validation
• Regression testing after corpus or prompt changes
• Compliance evidence for document-grounded AI answers

Security & quality

• Permission boundary testing is a security concern—not just quality
• Evaluation datasets should not contain production PII
• Automate regression checks in CI/CD for prompt and pipeline changes