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, cross-encoder reranking, and query transformation. The author reports achieving 95% recall@10 and 40% latency reduction by moving beyond default semantic search configurations.

• •Four chunking strategies (fixed, recursive, semantic, agentic) mapped to document types with measured recall@10
• •Hybrid retrieval using vector + BM25 + cross-encoder rerank via Reciprocal Rank Fusion improves recall 15%
• •Query transformation with LLM expansion handles poorly-formed user queries before retrieval

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