What you will learn
AI 101
A deep dive introduction to model capabilities, context design and engineering and experience evaluation.
1. Introduction
Welcome
Let's get started!
2. Getting our hands dirty
Context Design Exercise
Let's do some context design. The model's context window is the key to creating useful and helpful output.
Context Engineering Exercise
So we did some context design - now how does that become context engineering?
Evals Exercise
And the final missing piece: evals! But wait, do we even need them here?
3. Model basics
Models are Stateless
What does it mean for models to be stateless? Let's build some intuition around that.
Models are Stochastic
And what does it mean for models to be Stocastic? Why do they hallucinate? Can we ever get beyond that?
A model warning
Some common misunderstandings about AI and Large Language Models can easily lead us astray.
4. Let's write some evals
Evals intro: set up your accounts
This is what we came for: some hands-on eval writing.
An introduction to evals
What are evals, why do we need them, and why isn't this just QA?
Let's write an eval together
This is the fun part, hands-on writing evals together.
Eval Tips and Common Mistakes
Evals can be tricky, and it's easy to make some very expensive (in terms of quality, end result and cost) mistakes.
How we define what Good looks like
One reason evals are tricky, is that it can be hard to define what Good looks like when working (as we are) in a team.
Creating Data Sets
There are no evals without data sets. How do we create solid data sets? How many data points are enough? What about synthetic data?
5. Model Capabilities
Introduction to Model Capabilities
What can LLMs do? How do we know what the capabilities of these models are? How are they trained? And how does that influence our product design decisions?
Model Post Training and RLHF
How are capabilities trained into models? How can we build intuition around these capabilities and best use them?
Why do models have different personalities?
What is model character, how is it trained, and how can we learn to understand and use this beyond "Claude feels friendlier"?
Claude Code for UX People and Researchers
Despite the "code" in its name, Claude Code is perhaps the most popular agentic AI system right now. Understanding and using it gives you a glimpse into what's coming the coming months and years in terms of agents. And it can be incredibly useful for non-coding tasks.
1. Getting started with Claude Code, for non-engineers
Why should I try out Claude Code
I'm not an engineer, I don't write code, what can I learn from playing around with Claude Code?
Let's install Claude Code
And learn a few tricks along the way.
First Steps with Claude Code
Let's dive in and start creating.
We'll build a data analysis website
We'll take some really interesting data on the US supreme court hearings, and build a website to explore this data with Claude Code.
A clean redesign and slash commands
Let's redesign the search we built to be clean and minimalistic. Also, what are slash commands?
2. Gemini 3 and the Antigravity editor
From Claude Code to the Antigravity editor
We'll move from the command line, taking the app we built with Claude Code, and try out Google's Antigravity editor.
Can we build a chatbot on this data?
Let's try something harder - can we build a chatbot on top of this data? And get familiar with Google's Antigravity editor along the way.
Wrapping up our Antigravity experiment
Let's wrap up and review some lessons learnt.
Project Planning for AI
If AI is different, and AI projects are different, how do we plan projects for AI? What are the roles and tracks we should consider? What are some common gotchas?
1. Project Planning for AI
Project planning for Evals and Context Design
Context Design and Evals are two cornerstone activities to build great AI products. How do we plan for them?
Budgets for AI projects
How do we budget an AI project? What are some of the things to look out for?
AI project roles
AI is indeed different - what roles or skillsets should we hire for or plan for when preparing AI projects?
It's not just the videos.
The videos are hands-on, and come with links to tons of useful resources, prompts you can copy and paste, data sets you can use for the exercises and more.
Aside from that, you'll probably have some questions. It's tricky to wrap your head around this new world. How does vector search really work? Does AI really have a world model? You get a direct line to me on Slack for all your questions.
The third pillar of learning is community. No, we're not starting another Slack or Discord group. Instead, you'll have full access to regular group office hours calls. It's the best way to learn.