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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