LLM

You'll learn how RAG retrieves, augments and generates answers, why tuning retrieval won't fix outdated documents, and how to keep a knowledge base trustworthy by managing it like code.

You'll learn how a mid-sentence chunk split causes a RAG pipeline to confidently deny a fact that's actually in the source docs, and how to find and fix that failure in your own pipeline.

A team burned three weeks training a model before an afternoon prompt rewrite did the actual job — here's the order you're supposed to try things in, and the two questions that would've caught it before week one.

The answer your user needed didn't get lost in search — it got cut in half before your embedding model ever laid eyes on it, and there's a 20-minute check that proves it.