Kevin Crook

kevincrook.com

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My UC Berkeley Faculty Page

If you want to learn AI

If you want to teach AI

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Free AI Workshops for Beginners

Free, Non-commercial, No sales pitches.

Teens, Adults, Seniors.

I've been working in AI since the 1980s and teach online graduate courses for UC Berkeley remotely while based in Euless, Texas. Giving free AI workshops is a passion project for me. I offer them both face-to-face and virtually, to empower folks to truly understand and use AI. And since I can only be in one room at a time, I'm making every module free for others to teach — see Train the Trainer below.

Module: What is AI?

This module takes on a question that turns out to be harder than it sounds. We start with the formal definition, coined by John McCarthy at the 1956 Dartmouth conference, then look at why the answer keeps moving: whatever computers cannot do yet gets called AI, and once it is solved we stop calling it that. Along the way we cover the Turing Test, why the tasks that are effortless for people are the hardest for machines, whether asking if an AI thinks makes any more sense than asking if a submarine swims, what artificial general intelligence would actually mean, and whether there is any reason to believe these systems experience anything at all.

Module: AI Ethics & Safety

A practical tour of what can go wrong. The module covers the concerns that reach you directly, including privacy, bias, echo chambers, academic honesty, and the effect on entry level jobs, alongside the larger ones: the electricity and water AI consumes, the copyright fights still being settled in court, autonomous weapons, and cybersecurity, where AI is arming attackers and defenders at the same time. It closes on transparency and why it matters that most AI is a black box that can accuse you without explaining itself, and on the classic dystopias, from Asimov's Three Laws to the paperclip maximizer.

Module: Prompt Engineering for Beginners

This module comes in three parts. The first explains what is actually happening under the hood: prompts, tokens, context windows, why graphics cards turned out to be the right hardware for AI, and the failure modes worth recognizing, including hallucinations, losing information in the middle of a long document, and the context and task rot that creep into a long chat. The second is the practical core, built around meta-prompting combined with Role and COSTAR, the approach used by winners and top finishers in major prompt engineering contests. The third is a takeaway of 100 example prompts to adapt and reuse.

Module: Interesting Topics in AI

This module is a growing collection of white papers on interesting topics in AI. Some look back at the history of the field, others at what is happening right now. They work well as supplements to the other modules: several expand on ideas introduced elsewhere, and new ones will be added over time.

Module: Knowledge Graph Engineering for Beginners

A knowledge graph stores information as connected facts rather than loose text, and that structure addresses a specific weakness in today's AI. When a question takes several hops of reasoning, where A relates to B, which relates to C, which relates to D, the AI's accuracy degrades with every hop away from the original information. This module covers building knowledge graphs from your own material and then putting them to work, either directly in the context window or through retrieval, so the AI is handed the connections instead of having to infer them.

Module: LLM Engineering for Beginners

This module covers customizing an LLM using your own documents. It works through the main approaches side by side, including vector databases, graph databases, fine tuning, Retrieval-Augmented Generation (RAG), and Knowledge Graph RAG, so you can see what each one is good for and where the trade-offs are.

Train the Trainer (for Teachers)

For faculty and staff at high schools, colleges, universities, and public libraries: you are welcome to use my workshop materials in your own classes and in workshops you offer free of charge. Please keep the materials as-is without adaptation, and credit Kevin Crook, kevincrook.com. I also offer free virtual Train the Trainer workshops.

Disclaimers

I am a lecturer at the University of California, Berkeley, in the Master's Program in Data Science and the Master's Program in Cybersecurity. This website is my personal platform, and all content reflects my own views and opinions, which are not necessarily those of UC Berkeley.