RAG quality needs measurement beyond whether a demo answer sounds plausible. Teams should score retrieval relevance, source coverage, citation faithfulness, unsupported claims, and refusal behavior.
Evaluation datasets should include expected answers, edge cases, negative tests, and stale-record scenarios. These become release gates whenever prompts, chunking, models, or retrieval parameters change.
Observability closes the loop. Prompt traces, retrieved chunks, model versions, token spend, user corrections, and escalation rates reveal where the system is drifting.