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 proved inadequate for legal contracts, API docs, and customer support tickets. 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 combined vector search with BM25 keyword search using Reciprocal Rank Fusion, then added a cross-encoder reranking stage that improved recall by 15% at a cost of only 50ms. A Bayesian-informed query transformation layer was also introduced to handle poorly phrased user queries before retrieval begins. The cumulative result was a 40% reduction in end-to-end query latency compared to their original semantic-search-only baseline.

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Engineering team cuts RAG pipeline latency 40% using Bayesian search and hybrid retrieval

A development team rebuilt their retrieval-augmented generation (RAG) pipeline from scratch after standard production deployments proved inadequate for legal, API, and support-ticket use cases. 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%. They combined vector search with BM25 and cross-encoder reranking using Reciprocal Rank Fusion, which improved relevance correlation from roughly 0.75 to 0.92. A Bayesian-informed search optimization was also applied, ultimately reducing overall query latency by 40%. The team published their configurations and Python code to help other engineers replicate the tunable retrieval pipeline.

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

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