Back to feed
Dev.to
Dev.to
7/19/2026
Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking That Cut Latency 40%

Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking 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, 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, parallel retrieval, and a 50-to-5 rerank funnel. Includes production config tables and Python code for each pipeline stage.

  • •Chunking strategy should vary by document type: recursive for legal, semantic for conversations, agentic for complex wikis
  • •Hybrid retrieval (vector + BM25 + cross-encoder rerank) via Reciprocal Rank Fusion beats pure vector or keyword search
  • •Cross-encoder rerank costs 50ms but gains 15% recall; query transformation improves retrieval for poorly-formed user queries

Generated with AI, which can make mistakes.

#ai-tools#ai-agents#open-source
Read full article at Dev.to

Is this a good recommendation for you?

Comments

Failed to load comments. Please try again.

Explore more

Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking That Cut Latency 40%
Dev.to
Production RAG Optimization: Chunking Strategies, Hybrid Retrieval, and Reranking for 95% Recall@10
Dev.to
Production RAG Optimization: Chunking Strategies, Hybrid Retrieval, and Reranking for 95% Recall@10
Dev.to
The original title is: "Optimizing RAG at Scale: Chunking, Retrieval, and Hybrid Search That Cut Latency 40%"
Dev.to