Building a Production RAG Pipeline: Chunking Strategies, Hybrid Retrieval, and 40% Latency Reduction

Short summary

A detailed walkthrough of rebuilding a RAG retrieval pipeline for production, covering document-type-specific chunking strategies (recursive, semantic, agentic), hybrid retrieval combining vector search and BM25 with reciprocal rank fusion, and cross-encoder reranking to achieve 95% recall@10. The approach cuts latency by 40% by parallelizing retrieval stages and funneling 50 candidates down to 5 through a cross-encoder. Query transformation is also introduced to handle poorly-formed user questions.

• •Document-type-specific chunking (recursive for legal, semantic for tickets, agentic for wikis) beats fixed 512-token splits
• •Hybrid retrieval (vector + BM25 + RRF + cross-encoder rerank) achieves 95% recall@10 vs pure vector search
• •Cross-encoder reranking costs 50ms but gains 15% recall; parallel retrieval stages cut total latency 40%

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