For professionals
Why hands-on learning for professionals?
You don’t need to be a data scientist to understand how LLM (Large Language Model) A language model trained on a very large amount of text, with billions of parameters. The hand-built models in these lessons are tiny language models; ChatGPT, Claude, and Gemini are large language models. The core principles are identical---the difference is scale. View in glossary work. But if you’re making decisions about AI tools, leading teams that use them, or trying to cut through vendor hype, having a concrete mental model matters.
This workshop gives you that model through direct experience. You’ll build a language model by hand—counting word patterns in a text, recording them as tally marks or physical piles of paper cutouts, then rolling dice to generate new text. No programming required.
The result? You’ll understand what’s actually happening when you type a prompt into ChatGPT OpenAI's chatbot, and probably the most well-known LLM product. On this site we often use "ChatGPT" as shorthand for any modern LLM chatbot---the concepts apply equally to Claude, Gemini, DeepSeek and others. The underlying principles are the same regardless of which product you use. View in glossary , why these tools sometimes produce nonsense, and what the fundamental limitations are. You’ll be able to have informed conversations about AI adoption and help your team use these tools more effectively.
Suggested ways in
Every organisation is different, so which lessons you choose to learn will depend on your specific needs and goals. We suggest you start with the Fundamentals, and then choose-your-own adventure from there.
A few more facilitation tips:
- mixed technical groups: pair technical and non-technical participants for the training step—different perspectives often surface useful insights
- sceptics: the hands-on format works well for sceptics because it’s concrete—you’re not asking anyone to believe claims about AI, you’re showing them how patterns in text become generated output
- follow-up: after the workshop, participants often want to explore the full lesson library—send them the link and let them go deeper on topics that interest them