Engineering Team Cuts RAG Pipeline Latency 40% Using Bayesian Search and Hybrid Retrieval

A development team overhauled their Retrieval-Augmented Generation (RAG) pipeline after standard production deployments — using fixed 512-token chunks and basic vector search — proved inadequate for real-world use cases like legal contracts, API docs, and customer support tickets. The team rebuilt their retrieval layer using document-specific chunking strategies, including recursive, semantic, and agentic chunkers, achieving recall@10 scores between 91% and 97% depending on document type. They replaced pure vector search with a hybrid retrieval system combining vector search, BM25, and cross-encoder reranking via Reciprocal Rank Fusion, with the reranking step alone improving recall by 15%. A Bayesian-informed search optimization further reduced end-to-end query latency by 40%, bringing response times well below the original one-second threshold. The approach demonstrates that tuning chunking and retrieval strategies to specific content types, rather than applying uniform defaults, significantly improves both speed and accuracy in production RAG systems.