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Dev.to
7/20/2026
The original title is: "Optimizing RAG at Scale: Chunking, Retrieval, and Hybrid Search That Cut Latency 40%"

The original title is: "Optimizing RAG at Scale: Chunking, Retrieval, and Hybrid Search That Cut Latency 40%"

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

Short summary

A detailed engineering deep-dive on rebuilding a production RAG retrieval layer from first principles, covering chunking strategies by document type, hybrid retrieval combining vector search and BM25 with reciprocal rank fusion, and cross-encoder reranking. The author reports achieving 95% recall@10 and 40% latency reduction through query transformation, tunable retrieval pipelines, and document-aware chunking. Includes production config tables and Python code for each component.

  • •Document-type-aware chunking (recursive, semantic, agentic) beats fixed 512-token splits
  • •Hybrid retrieval (vector + BM25 + cross-encoder rerank) via reciprocal rank fusion improves recall 15%
  • •Query transformation before retrieval handles poorly-formed user questions

Generated with AI, which can make mistakes.

#ai-tools#ai-agents#open-source
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Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking That Cut Latency 40%
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Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking That Cut Latency 40%
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Production RAG Optimization: Chunking Strategies, Hybrid Retrieval, and Reranking for 95% Recall@10
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Production RAG Optimization: Chunking Strategies, Hybrid Retrieval, and Reranking for 95% Recall@10
Dev.to