What is Retrieval Accuracy?
Retrieval accuracy refers to how well a retrieval system finds relevant information while avoiding irrelevant results. It's typically measured using metrics from information retrieval like precision (what fraction of retrieved results are relevant), recall (what fraction of all relevant items were retrieved), and derived metrics like F1 score, mean average precision (MAP), or normalized discounted cumulative gain (NDCG). High retrieval accuracy is crucial for RAG systems and agent memory, as poor retrieval directly leads to poor generation quality.
Measuring retrieval accuracy requires ground truth data: queries with known relevant results or human judgments of relevance. For production systems, this might come from manual labeling of sample queries, user feedback signals (clicks, ratings), or synthetic evaluation sets. The measurement process retrieves results for test queries, compares them against ground truth, and computes metrics. Regular evaluation helps track system quality, identify degradation, and measure the impact of changes.
Improving retrieval accuracy involves multiple strategies: better embedding models that capture semantic similarity more accurately, hybrid search combining multiple retrieval methods, query preprocessing and rewriting to better match how information is expressed, re-ranking to refine initial results, and careful tuning of system parameters. The optimal approach depends on the specific application, data characteristics, and whether errors of omission (missing relevant results) or commission (including irrelevant results) are more problematic. Continuous monitoring and optimization of retrieval accuracy is essential for maintaining RAG system quality.