Generative AI has moved from research labs into everyday tools remarkably quickly. Before diving into a course, it helps to have a clear, simple mental model of what's actually happening.
What a large language model actually does
At a basic level, a large language model predicts the most likely next piece of text given everything that came before it. Trained on huge amounts of text, this simple mechanism turns out to be powerful enough to answer questions, summarise documents and write code.
Why prompting is a real skill
Because these models respond to the exact wording and structure of a request, how you ask matters. Clear instructions, relevant examples and well-structured context consistently produce better, more reliable answers.
Where RAG fits in
Retrieval-augmented generation (RAG) connects a model to your own documents or data at the moment you ask a question, so it can answer using specific, up-to-date information instead of only what it learned during training.
How this shows up in our course
Our Generative AI track starts with these fundamentals before moving into building small, working applications — so students understand not just how to call an API, but why a particular prompt or setup works better than another.