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, 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 these techniques. Includes production config tables and Python code for each component.

• •Chunking strategy must vary by document type: recursive for legal, semantic for tickets, agentic for wikis
• •Hybrid retrieval (vector + BM25 + cross-encoder rerank) beats either approach alone, gaining 15% recall for 50ms
• •Query transformation before retrieval improves results because users ask badly

Generated with AI, which can make mistakes.