retrieval metrics

**Retrieval metrics** is the **evaluation framework for measuring how well a retriever finds and ranks relevant documents for user queries** - these metrics quantify retrieval quality before and alongside end-to-end answer evaluation. **What Is Retrieval metrics?** - **Definition**: Statistical measures such as recall, precision, MAP, NDCG, MRR, and hit rate. - **Evaluation Inputs**: Query sets paired with relevance labels or ground-truth evidence mappings. - **Metric Families**: Coverage metrics, rank-sensitive metrics, and graded-relevance metrics. - **Usage Context**: Applied during model selection, tuning, and production regression monitoring. **Why Retrieval metrics Matters** - **Quality Visibility**: Identifies whether failures originate in retrieval or generation stages. - **Optimization Guidance**: Different metrics expose different tradeoffs in ranking behavior. - **Release Safety**: Prevents unnoticed retrieval regressions after index or model changes. - **Benchmark Comparability**: Enables objective retriever comparisons across experiments. - **RAG Reliability**: Strong retrieval metrics correlate with better grounded answer quality. **How It Is Used in Practice** - **Labeled Dataset Curation**: Build representative query-relevance test sets by domain. - **Metric Portfolio**: Track multiple metrics to avoid over-optimizing one signal. - **Continuous Tracking**: Monitor metric drift in production and trigger re-tuning when needed. Retrieval metrics is **a core evaluation layer for search and RAG systems** - disciplined measurement is required to improve retriever quality and maintain reliable evidence delivery.