What is RAG evaluation? Measuring retrieval quality and answer groundedness

Retrieval-Augmented Generation (RAG) systems aim to generate grounded responses by retrieving relevant documents from a knowledge base and using this context in language models. However, RAG pipelines can fail silently, returning unrelated documents or generating hallucinated facts, highlighting the need for systematic RAG evaluation. This evaluation involves measuring the quality of both retrieval and generation stages independently to diagnose and fix issues, using metrics such as context precision and recall, answer groundedness, and faithfulness. Effective RAG evaluation requires both offline testing with curated datasets and online monitoring of real-world queries to capture unexpected variations in user input. Braintrust provides a comprehensive platform for RAG evaluation, integrating tracing, scoring, experimentation, and monitoring to ensure consistent measurement and improvement of RAG pipeline quality across development and production environments.