Building a Retrieve-Augmented Generation (RAG) pipeline is a significant step, but determining its effectiveness and refining its performance are necessary follow-up actions. A RAG system that retrieves irrelevant information or generates inaccurate responses is of limited practical use.

This chapter focuses on the methods used to assess the quality of your RAG system. You will learn about:

• Common difficulties encountered when evaluating RAG outputs.
• Approaches to evaluate the individual components: the retriever's ability to find relevant context and the generator's ability to produce faithful and relevant answers based on that context.
• Metrics used for assessing retrieval (like Hit Rate or Mean Reciprocal Rank, $MRR$) and generation quality.
• Identifying typical failure points in RAG systems.
• Basic techniques to enhance performance, such as adjusting data chunking strategies or refining prompts.

By the end of this chapter, you will have a foundational understanding of how to measure the performance of your RAG system and apply initial strategies for improvement.