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

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A development team overhauled their Retrieval-Augmented Generation (RAG) pipeline after standard production deployments revealed significant performance gaps with legal, API, and support ticket documents. The team replaced fixed 512-token chunking with document-type-specific strategies, including recursive, semantic, and agentic chunking, achieving recall@10 scores between 91% and 97% across content types. They also implemented a hybrid retrieval system combining vector search and BM25 keyword search, fused via Reciprocal Rank Fusion and re-ranked using a cross-encoder model. The cross-encoder reranking step added only 50ms of latency while improving recall by 15%, contributing to an overall 40% reduction in end-to-end query latency. The team noted that query transformation and parallel retrieval stages were equally critical to reaching production-grade performance.

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