Hybrid Search and Reranking

Hybrid search reranking combines keyword-based (sparse) and semantic (dense) retrieval to dramatically improve RAG answer quality. Traditional sparse retrieval using BM25 excels at exact-match precision; dense embedding search captures semantic meaning but can miss critical lexical matches. By fusing both methods and applying cross-encoder reranking, you create a multi-stage pipeline that balances recall and relevance, reducing hallucination and ensuring answers ground firmly in retrieved documents.

This series teaches you how to implement production-grade hybrid pipelines: from understanding BM25 and dense vectors separately, through fusion algorithms like reciprocal rank fusion, to advanced techniques like query expansion and multi-stage coarse-to-fine ranking. Each article is a complete, hands-on tutorial with code examples and tuning guidance. By the end, you will be able to architect hybrid retrieval systems that consistently outperform single-method approaches, meeting real-world latency and accuracy demands.

Articles in this series

• What Is Hybrid Search? Combining Keywords and Embeddings
• BM25 Keyword Retrieval: Sparse Search Fundamentals
• Dense Vector Search: Embedding-Based Semantic Retrieval
• Combining Sparse and Dense: Fusion Strategies for Hybrid Retrieval
• Reciprocal Rank Fusion: Merging Rankings by Position
• Cross-Encoder Reranking: Pairwise Relevance Scoring
• Implementing Reranking in Production RAG Pipelines
• Query Expansion: Generating Better Search Terms for Hybrid Search
• Tuning Hybrid Search Weights and Retrieval Parameters
• Multi-Stage Retrieval: Coarse-to-Fine Ranking Architectures