RAG Evaluation Cheat Sheet

Retrieval-Augmented Generation (RAG) evaluation is the systematic process of measuring how effectively a RAG system retrieves relevant context from a knowledge base and generates accurate, grounded responses. Unlike traditional LLM evaluation, RAG assessment requires evaluating both retrieval quality (did we find the right documents?) and generation quality (is the answer accurate and faithful to the retrieved context?). The core challenge lies in the dual nature of failure modes—answers can be wrong because retrieval missed key information, because the generator hallucinated despite good context, or because the context itself was noisy, irrelevant, or stale. A comprehensive evaluation strategy extends beyond the standard four metrics (faithfulness, answer relevance, context precision, context recall) to include context trustworthiness, agentic task completion, multi-turn conversation quality, and security robustness—often implemented via automated metrics, LLM-as-a-judge techniques, CI/CD quality gates, and production runtime guardrails.