Retrieval-augmented generation (RAG) systems rely on effective retrieval to produce accurate answers. This analysis provides a practical framework for benchmarking RAG pipelines using BM25, embedding-based retrieval, and reranking methods. It emphasizes the importance of citation evaluation to ensure that generated responses are grounded in retrieved evidence. For teams building RAG applications, establishing solid baselines is crucial for measuring improvements and selecting the right retrieval strategy. The article discusses trade-offs between lexical and semantic retrieval, and how reranking can bridge the gap. Understanding these evaluation techniques helps developers optimize their systems for accuracy and reliability. As RAG becomes a standard pattern in LLM applications, having robust evaluation practices is essential for delivering trustworthy AI products.
Learn how to evaluate RAG systems with BM25, embedding retrieval, and reranking, including citation-based quality assessment.