relevance scoring

**Relevance scoring** is the **assignment of numeric relevance values to retrieved candidates based on query-document match quality** - these scores drive ranking, filtering, and context selection decisions. **What Is Relevance scoring?** - **Definition**: Quantitative estimate of how well a candidate document or passage answers a query. - **Score Sources**: Lexical models, embedding similarity, cross-encoder logits, or hybrid fusion outputs. - **Decision Use**: Rank ordering, threshold filtering, and reranking candidate prioritization. - **Calibration Need**: Raw scores may not be directly comparable across models or query types. **Why Relevance scoring Matters** - **Ranking Quality**: Better scoring directly improves top-k evidence accuracy. - **Noise Filtering**: Score thresholds remove low-signal candidates that increase hallucination risk. - **Pipeline Efficiency**: Focuses expensive reranking on high-potential candidates. - **Robustness**: Stable scoring improves consistency across query distributions. - **Diagnostics**: Score distributions reveal retriever drift and domain mismatch. **How It Is Used in Practice** - **Score Normalization**: Align heterogeneous scores before hybrid fusion. - **Threshold Tuning**: Set minimum relevance cutoffs by domain and risk tolerance. - **Monitoring**: Track score drift over time and retrain retrievers when degradation appears. Relevance scoring is **the ranking signal backbone of retrieval systems** - accurate, calibrated scoring is essential for high-quality evidence selection and reliable grounded generation.