RAG-Specific Evaluation with RAGAS
~14 min read
RAG systems have two failure surfaces — retrieval and generation — so they need metrics that isolate each. RAGAS measures faithfulness, answer relevancy, context precision and context recall, often without needing gold answers.
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Key points
- •RAG can fail at retrieval (wrong docs) or generation (ignores good docs) — componentized metrics isolate which half to fix
- •Faithfulness checks every answer claim against the retrieved context — the direct measure of grounding vs hallucination
- •Answer relevancy checks the answer actually addresses the question; both are generation-side metrics
- •Context precision (are retrieved chunks relevant and well-ranked?) and context recall (did we fetch all needed info?) are retrieval-side metrics
- •Many RAGAS metrics use an LLM to judge question/context/answer relationships, so several need no human-written gold answer