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Continuous RAG evaluation catches the quiet kind of failure: the system never goes down, never returns errors, never trips a latency alert, it simply answers worse as the index, the model, and user questions drift. Track retrieval precision, effective recall, faithfulness to the retrieved context, answer relevance, and p99 latency.
Measuring RAG quality rigorously takes more than skimming a handful of answers: it requires objective metrics (faithfulness, relevance, context precision, and coverage), a golden set of hundreds of curated questions, and regular human validation of the LLM judge to avoid misleading conclusions.
Evaluating a RAG system without metrics is pure guesswork. Ragas measures four core signals: faithfulness, answer relevancy, context precision and context recall, using an LLM as judge. TruLens, DeepEval and other frameworks cover similar ground. Wiring evaluation into CI from day one catches regressions in prompts, chunking or model choice before they reach production.
