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Dev.to
7/21/2026
Optimizing RAG Retrieval: Chunking Strategies, Hybrid Search, and Reranking for 95% Recall@10

Optimizing RAG Retrieval: Chunking Strategies, Hybrid Search, and Reranking for 95% Recall@10

Original: Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%

Short summary

Duplicate of an earlier post covering production RAG optimization: chunking strategies (fixed, recursive, semantic, agentic) tuned per document type, hybrid retrieval combining vector search and BM25 with reciprocal rank fusion, and cross-encoder reranking achieving 95% recall@10 with 40% latency reduction. Includes Python code for all components and a production config table with measured recall metrics.

  • •Four chunking strategies with per-document-type production configs achieving 91-97% recall@10
  • •Hybrid retrieval combining vector search and BM25 via reciprocal rank fusion plus cross-encoder reranking
  • •Query transformation step to handle poorly-formed user questions before retrieval

Generated with AI, which can make mistakes.

#ai-tools#ai-agents#open-source
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Optimizing RAG Retrieval: Chunking Strategies, Hybrid Search, and Reranking for 95% Recall@10
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