Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking 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 with concrete code examples and a production config table mapping document types to optimal chunk sizes and overlap values.

• •Four chunking strategies (fixed, recursive, semantic, agentic) with per-document-type production configs achieving 91-97% recall@10
• •Hybrid retrieval using parallel vector + BM25 search with reciprocal rank fusion and cross-encoder reranking improves recall by 15%
• •Query transformation via LLM expansion addresses poorly-formed user queries before retrieval

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