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 document-type-specific chunking strategies (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 moving beyond naive fixed-token chunking and pure vector search.

• •Document-type-specific chunking (recursive, semantic, agentic) beats fixed 512-token splits
• •Hybrid retrieval (vector + BM25 + reranker) with RRF outperforms either method alone
• •Cross-encoder reranking adds 50ms but gains 15% recall; query transformation handles bad user queries

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