Optimizing RAG at Scale: Chunking Strategies, Hybrid Retrieval, and Reranking for 95% Recall

Short summary

A detailed engineering breakdown of rebuilding a production RAG pipeline from first principles, covering chunking strategies (recursive, semantic, agentic) tuned per document type, hybrid retrieval combining vector search and BM25 with reciprocal rank fusion, and cross-encoder reranking. The author reports achieving 95% recall@10 and 40% latency reduction through these optimizations.

• •Different document types need different chunking strategies—recursive for legal, semantic for tickets, agentic for wikis
• •Hybrid retrieval (vector + BM25 + cross-encoder rerank) via reciprocal rank fusion outperforms pure vector search
• •Cross-encoder reranking costs 50ms but gains 15% recall; query transformation handles poorly-formed user queries

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