RAG optimization cuts latency 40%

The team replaced a naïve 512-token fixed chunker and single-modality vector search with a document-type-aware chunking layer, a hybrid BM25-vector retriever, and a Bayesian optimizer that tuned chunk size, overlap, and weight ratios [DevTo]. The new pipeline reports 95% recall@10 and a 320 ms p95 latency, a 62% drop from the original 850 ms latency, while cutting cost per query from $0.008 to $0.005.

The team used different chunking strategies for different document types, including a recursive clause-aware chunker for legal contracts, a function-aware recursive chunker for API references, and a semantic similarity chunker for support tickets [DevTo].

The hybrid retriever combines a vector store, a BM25 index, and a cross-encoder reranker with weights (0.4, 0.3, 0.3) [DevTo]. The cross-encoder improves recall from 78% (single query) to 96% (five expanded queries) at a 50 ms overhead.

Using Optuna’s TPE sampler, 100 trials explored categorical chunk sizes, overlap ranges, top-k values, and weight ratios on a 200-query golden set [DevTo]. The Pareto frontier identified three production profiles: Conservative (91% recall, 180 ms latency), Balanced (95% recall, 320 ms latency), and Aggressive (97% recall, 580 ms latency).

The new pipeline achieves sub-second latency for most workloads, meeting typical SLA thresholds for interactive chat assistants [DevTo]. Hallucinations fell from 12% to 3% after hybrid retrieval and query expansion, a 75 pp improvement that directly impacts user trust [DevTo]. Reducing the per-query cost to $0.005 makes large-scale deployments viable for enterprises that process millions of queries daily [DevTo].