Retrieval-Augmented Generation (RAG) systems have become foundational architecture for enterprise AI applications, enabling large language models to access external knowledge sources and provide grounded, context-aware responses. However, evaluating RAG performance presents unique challenges that differ significantly from traditional language model evaluation. Research from Stanford's AI Lab indicates that