AI 101
A deep dive introduction to model capabilities, context design and engineering and experience evaluation.
What we will discuss:
- What is context design?
- Let's try an eval.
- What is context engineering?
Understanding models
- Models are stochastic.
- Models are stateless.
- How should I think about model capabilities?
- Why do models have different personalities?
Who is this for?
- Anyone trying to better understand LLMs.
- Non-engineers who want to learn the basic activities that build great AI experiences.
- Team leaders that are looking for some grounding in the underlying technology.
AI 101
Build a deeper understanding of AI. Why do models have a personality? What is context engineering?
- All 10 videos in this series
- Prompts, templates and resources from each episode
- Watch at your own pace, on any device
- Full access to every other course on model context experience
Full access to all courses, $79/m after the trial. Cancel anytime.
What you'll learn
- How models actually work: stateless, stochastic, and why they hallucinate
- Context design and context engineering, hands-on
- What evals are, why they are not QA, and how to write your first one
- How to define what "good" looks like as a team
- How to build data sets, including synthetic data
- What model capabilities and character are, and how to use them in product decisions
Learn from Peter
Peter Van Dijck
Peter has been building AI products since 2023 and teaching teams how to build them since 2024. He has spent that time in the trenches with evals, observability, synthetic data and context engineering, and turns what actually works into practical, no-fluff lessons.
Every episode is taught by Peter himself, in plain language, for product managers, designers, researchers and strategists who need to understand how AI systems are really built, without needing to be an engineer.
Peter on LinkedInWho this is for
Product managers
You own an AI feature and need to make confident decisions about models, context and quality.
UX designers and researchers
You design or study AI experiences and want to understand the system underneath the interface.
Strategists and leads
You need enough hands-on understanding to steer an AI team without being an engineer.
What's included
- All 10 videos in this series
- Prompts, templates and resources from each episode
- Watch at your own pace, on any device
- Full access to every other course on model context experience
- Three practical exercises: context design, context engineering and evals
- A first eval written together, step by step
What participants say
“I have tried a few AI courses and most of them were either aimed at developers or were basically a sales pitch. This one is made for people like me (UX researcher, not technical) and it explains what is actually going on without dumbing it down. I now feel like I can follow the conversation when engineering talks about models and context and I can push back when something doesn't make sense for users. Peter's explanations are really clear, I was surprised by how much ground the course covers.”
“This was the missing piece for me. I understood the tools on a surface level but not why they behave the way they do, and that was making me nervous in product discussions. The videos are short and useful, and I have gone back to a few of them more than once.”
“This is opening up all sorts of new neural pathways for me to see under the hood more of how the sausage is made! 🙏”
The videos
1. Introduction
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. 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"?
Also included
Your subscription unlocks every course on model context experience.
Evals 101
How do we know if our AI systems are working well? *The* key skill for UX researchers and product people.
Course details →Claude Code for non-engineers
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.
Course details →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?
Course details →Designing with Claude
A hands-on walkthrough of Claude Design — Anthropic's tool that creates real, code-based designs. Set up a design system, generate and refine a landing page, and see where designing-by-code shines: interactive, animated, production-quality design with a design-to-engineering handoff measured in minutes.
Course details →Evals for agents
How do you build evaluations for agents? Model capabilities are evolving fast, user expectations are shifting, and both inputs and outputs are highly variable. This series walks through how to think about agent evals — from the kinds of agents you might be building, to identifying risk, defining quality, and combining qualitative research with metrics.
Course details →Content Strategy for LLMs
Content strategy is changing now that LLMs are reading, writing, and rewriting most of what we publish. This series is a practical walkthrough for content folks: setting up the right tools, structuring content as markdown, defining tone of voice and microcopy in ways an LLM can actually follow, and evaluating what comes out the other end.
Course details →