Retrieval-augmented generation (RAG) systems aim to enhance language model responses by grounding them in relevant documents, but they often encounter challenges such as irrelevant document retrieval, context hallucination, and factually correct but contextually irrelevant answers. Unlike standard LLM evaluation focusing on output quality, RAG evaluation requires assessing the entire pipeline, including retrieval quality, context utilization, and the grounding of answers in source documents. Key evaluation metrics include answer relevancy, faithfulness to retrieved context, context precision, and recall, which measure how well the system retrieves and uses relevant documents to answer questions accurately. Braintrust facilitates RAG evaluation by providing tools for tracing pipeline steps, creating evaluation datasets from real user queries, and employing various scorers to assess different quality dimensions. Continuous evaluation and iteration, including testing retrieval and generation separately and monitoring production performance, are essential for improving RAG systems, as real-world usage reveals edge cases and challenges not apparent in development.