RAG & Custom AI Knowledge

What you'll be able to do

You will be able to build a system that answers questions from your own documents rather than from a model's training data — chunking source material sensibly, embedding and retrieving it, and assembling the context the model sees.

Just as importantly, you will be able to tell when your retrieval is the problem rather than the model, which is where most RAG debugging time goes and where most of it is spent looking in the wrong place.

Who it's for

Developers and technical marketers building question-answering over a document set — internal knowledge bases, documentation, support archives, research collections.

Who it isn't for

Not for someone who has not yet built anything with an AI API. RAG is a systems problem, and this course assumes you are comfortable with the idea of calling a service, storing results and debugging a pipeline.

Also not necessary for small document sets. If everything you need fits in a modern context window, putting it in the prompt directly is simpler, more accurate and cheaper than building retrieval — and the course says so before teaching you to build it anyway.

What you'll need

Comfort with an API and a scripting language. An AI provider key. A set of documents you actually want to query, which makes every exercise real rather than academic.

How it's taught

Built around the failures that make RAG systems useless in practice: chunks that split a thought in half, retrieval that returns plausible but irrelevant passages, and the counterintuitive one — that retrieving more context frequently makes answers worse, because attention is finite and irrelevant material competes for it.

Evaluation is treated as part of building rather than as an afterthought, because a RAG system with no measurement is one you cannot improve deliberately.