Most RAG systems don't fail because you picked the wrong vector database. They fail the first time someone trusts an answer that's confidently wrong, with a citation pointing to a document that never said that. That gap, between a demo that works on stage and a system that survives a deploy, is what I spent the last months writing about. It's now a book, and it's out.
It walks the whole pipeline with runnable code against a single example corpus: embeddings, chunking, vector storage, ingestion, hybrid retrieval, reranking, query transformation, evaluation, and hardening for production. Not another LangChain tutorial. The real production tradeoffs, the boring-correct defaults, and the failure modes nobody shows you in the demo.
It's for engineers who are actually building this and want to understand why their pipeline behaves the way it does, instead of copy-pasting a framework and hoping it holds up in front of real users.
It's on Leanpub now. Buy it once and you get every revision as I keep working on it. If you're building RAG and something in it tips a decision on your own project, I'd want to know what. You can always send me an email.