What belongs on a RAG evaluation checklist?
A useful RAG evaluation checklist covers retrieval metrics, groundedness, permission tests, refusal behaviour, latency/cost, and regression canaries. Nabhi Labs applies these before production rollout.
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NABHI LABS
RAG evaluation checklist
Use this checklist before you scale a retrieval-augmented system. Nabhi Labs uses the same lenses on custom RAG implementations: evidence, permissions, and measurable groundedness.
Target words: rag evaluation · rag checklist · rag groundedness · retrieval metrics
Retrieval quality
Define gold questions with known source passages. Measure recall at k, precision of cited chunks, and failure on adversarial or out-of-scope queries. Hybrid lexical + dense retrieval should beat either alone on your corpus before you celebrate demos.
Track freshness: stale indexes silently degrade trust. Version corpora and embeddings so regressions are attributable.
Groundedness and safety
Score answers for support by retrieved evidence. Require refusal when evidence is insufficient. Separate policy content from speculative generation. Align risk discussions with the NIST AI Risk Management Framework and OECD AI Principles.
Permissions must be tested as first-class: a user must never retrieve a chunk they cannot open in the source system.
Production readiness
Instrument latency, cost per query, citation click-through, and human override rates. Add canaries for prompt or model changes. Document ownership of evaluation sets the way you document on-call.
When you want Nabhi Labs to run this as an engagement, start at the RAG engineering service page or email hello@nabhilabs.com.
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Citations & further reading
Related
Start with what feels complex.
Email hello@nabhilabs.com or send a note on the contact page. Nabhi Labs usually replies within 24 hours for a thoughtful discussion.
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