Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%

Short summary

A practical guide to optimizing RAG pipelines beyond the default 512-token chunking approach. Covers four chunking strategies (fixed, recursive, semantic, agentic) with per-document-type configs, hybrid retrieval combining vector search and BM25 with reciprocal rank fusion, and cross-encoder reranking that improves recall by 15% at 50ms cost. Claims 95% recall@10 and 40% latency reduction in production.

• •Four chunking strategies mapped to document types: recursive for legal/API docs, semantic for tickets, agentic for wikis
• •Hybrid retrieval with vector + BM25 + cross-encoder rerank via reciprocal rank fusion beats either method alone
• •Query transformation and structured reranking pipeline achieves 95% recall@10 with 40% latency reduction

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