Most "chat with your documents" demos work in an afternoon. Then you hit the last 20%: retrieval that...
Most "chat with your documents" demos work in an afternoon. Then you hit the last
20%: retrieval that misses the right passage, an LLM that confidently makes things
up, a reranker that wrecks your latency, chunking you re-tune ten times. And if
your documents are sensitive — legal, medical, internal — you can't just paste
them into a cloud API.
Build a RAG pipeline from scratch: chunking, embeddings, retrieval, reranking, grounded generation, and the production patterns…
RAG SOTA: I Tested 7 Pipelines and Built SEQUOIA (Open Source) After 20+ hours of compute...
Chunking sounds like the boring part of RAG. It is also where a lot of retrieval quality is won or...
By Naveen Ayalla This article is adapted from my original post in the Databricks Community and is...
Your RAG demo works on localhost. Under real load—socket exhaustion, 429s, and pool timeouts kill it. A frontend dev's…
Three weeks ago I needed a way to query a large document corpus without sending everything to an LLM...
