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 or irrelevant. A comprehensive evaluation strategy measures context relevance, answer faithfulness, retrieval precision, and generation accuracy, often using a combination of automated metrics, LLM-as-a-judge techniques, and targeted human evaluation to catch subtle quality issues that automated scoring may miss.