Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%
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Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10 The RAG Reality Check Everyone ships RAG the same way: chunk by 512 tokens, embed with text-embedding-3-small , top-k=5, stuff into context. It works for demos. Then you hit production: Legal contracts: 512 tokens splits clauses mid-sentence API docs: 1000-token chunks drown signal in noise Customer…
1Key Takeaways
- Then you hit production: Legal contracts: 512 tokens splits clauses mid-sentence API docs: 1000-token chunks drown signal in noise Customer….
- Headline: Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%
- Category focus: Coding AI — relevant for AI builders and decision-makers.
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — AI reports that then you hit production: Legal contracts: 512 tokens splits clauses mid-sentence API docs: 1000-token chunks drown signal in noise Customer…
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