Optimizing RAG at Scale: Chunking Strategies, Hybrid Retrieval, and Reranking That Cut Latency 40%

Short summary

A detailed engineering walkthrough of rebuilding a production RAG retrieval pipeline, 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 approach achieved 95% recall@10 and 40% latency reduction, with concrete code examples and a comparison table of strategies per document type.

• •Four chunking strategies mapped to document types: recursive for legal/API docs, semantic for tickets, agentic for wikis
• •Hybrid retrieval: parallel vector + BM25, reciprocal rank fusion, then cross-encoder rerank (50→5 funnel adds 15% recall for 50ms)
• •Query transformation via LLM to handle poorly-formed user questions before retrieval

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