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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 walkthrough of rebuilding a RAG retrieval layer for production, covering chunking strategies by document type (recursive, semantic, agentic), 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 by replacing naive fixed-chunk semantic search with a tunable, multi-stage pipeline. 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 to 95%
  • •Query transformation expands poorly-formed user queries before retrieval

Generated with AI, which can make mistakes.

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Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking That Cut Latency 40%
Dev.to
The original title is: "Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking That Cut Latency 40%"
Dev.to
Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking That Cut Latency 40%
Dev.to
The original title is: "Production RAG optimization: chunking strategies, hybrid retrieval, and reranking that cut latency 40%"
Dev.to