RAG Evaluation

Learn to measure RAG faithfulness — whether an AI answer is grounded in the context you gave it.

Learn context precision — the fraction of retrieved chunks that actually matter — and why noisy retrieval kills answer quality even when faithfulness looks fine.

Master answer relevance — measuring whether the model's response actually answers what the user asked, independent of faithfulness or retrieval quality.

Learn context recall — measuring whether your retrieval captured all the information needed to give a complete answer — and why high precision can mask dangerous recall gaps.

Learn to run a full end-to-end RAGAS evaluation, read the combined scorecard, diagnose which component to fix, and build a production eval pipeline that runs automatically.