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 revealed critical failures across legal, API, and support document types. The team replaced fixed 512-token chunking with document-specific strategies — including recursive, semantic, and agentic chunking — achieving recall@10 scores between 91% and 97% depending on content type. They combined vector search with BM25 and cross-encoder reranking using Reciprocal Rank Fusion, reducing reliance on any single retrieval method and improving relevance correlation from roughly 0.75 to 0.92. A query transformation layer was also added to handle poorly formed user queries before retrieval begins. The cumulative changes resulted in a 40% reduction in end-to-end query latency compared to the original pipeline.