This article was originally published on AI Study Room . For the full version with working code examples and related articles, visit the original post. RAG Evaluation: Retrieval Metrics, Generation Quality, End-to-End Testing, and Datasets Retrieval-Augmented Generation is the most popular architecture for production LLM applications. But evaluating RAG systems is notoriously difficult. You need to assess both the retrieval component and the generation component, then measure how they work together. Here is the practical evaluation framework. Retrieval Evaluation Retrieval quality determines the ceiling on your RAG system's performance. If the retriever fails to find relevant documents, the generator cannot produce good answers regardless of model quality. The primary retrieval metrics are hit rate and mean reciprocal rank. Hit rate measures whether the relevant document appears in the top-k results. MRR measures the rank position of the first relevant result.…