How to Evaluate Retrieval Quality in RAG Pipelines: Precision@k, Recall@k, and F1@k

Evaluating the retrieval component of a Retrieval-Augmented Generation (RAG) pipeline is crucial for ensuring meaningful answers are generated. The process involves distinguishing between binary and graded relevance measures and understanding outcomes like true positives and false negatives. Key order-unaware metrics for this evaluation include Precision@k, which measures the proportion of retrieved items that are relevant, and Recall@k, which measures the proportion of all relevant items that are successfully retrieved. The F1@k score then combines both precision and recall to provide a single, balanced measure of the retrieval system's performance.