Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency...
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:
Most RAG tutorials get you 70% of the way there. This is about the other 30% that actually matters in...
Explore common challenges and practical solutions for RAG retrieval systems at scale.
Learn why fixed-size chunking caps RAG retrieval quality, how adaptive strategies fit into your context infrastructure, and how…
Key Takeaways RAG pipeline chunking strategies determine retrieval quality more than the embedding...
Learn which chunking strategy fits your RAG pipeline—fixed-size, recursive, semantic, or LLM-driven—and how chunk size affects…
RAG (Retrieval-Augmented Generation) is the foundation of knowledge-grounded AI. But most RAG...
