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

Short summary

A detailed engineering walkthrough of optimizing RAG pipelines for production, covering document-type-aware chunking strategies (recursive, semantic, agentic), hybrid retrieval combining vector search and BM25 with cross-encoder reranking, and LLM-based query expansion. The author achieves 95% recall@10 and 40% latency reduction by treating retrieval parameters as a black-box function optimized via Bayesian search with Optuna. Includes production-ready Python code for each component along with benchmarked results across legal contracts, API docs, and support tickets.

• •Document-type-aware chunking (recursive, semantic, agentic) beats fixed 512-token splits across legal, API, and support content
• •Hybrid retrieval with reciprocal rank fusion and cross-encoder reranking improves recall by 15% over pure vector search
• •Bayesian optimization over chunk size, overlap, top-k, and weights via Optuna cut latency 40% while reaching 95% recall@10

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