Spoken summary — press play to read along: the line being spoken stays near the top.
What you'll learn
Estimated time: 15 minutes
By the end of this lesson you'll be able to:
- Understand what this course covers and how it's structured
- Explain why this material is durable even as models and products keep changing
- See how the Capabilities & Limitations framework and the 4D Framework work together
Welcome to the AI Capabilities & Limitations Course
(4 minutes)
The 4D Framework teaches YOU how to collaborate with AI. This course teaches you how AI is able to work with you. Together they're one system: human competencies on one side and machine properties on the other.
A mental model of the machine
Course roadmap
What we mean by AI
What is generative AI and how does it differ from other types of AI?
How AI is trained
How do pretraining and fine tuning give AI its character?
Properties of AI
What are next token prediction, knowledge, working memory, and steerability?
Putting it all together
What happens when properties collide in real life situations?
Next steps
How do you use this knowledge to use AI safely, effectively, and ethically?
Key takeaways
- The AI Fluency Framework (4Ds) describes human competencies. This course describes the machine properties those competencies respond to.
- Generative AI has four core properties: Next Token Prediction, knowledge, working memory, and steerability.
- This material is durable because the properties stay stable even as models improve. Boundaries shift but the properties remain the same.
Exercises
Exercise: Mapping Your Current AI Use
Why? This is the foundation for every exercise that follows in this course.
- **List 4–6 tasks you've actually used AI for in the last two weeks. ** If you haven't used AI much yet, list tasks you'd like to use it for. Be specific: "drafted a client email explaining a project delay" tells you something. "Writing" doesn't.
- For each task, note one line: did the output land on the first try, or did you need to rework it before it was usable? Don't overthink this. A quick gut check is fine.
- Now share your list with Claude (or any AI assistant) and ask: "For each of these tasks, what's one way this could go wrong if I'm not paying attention? " See if the failure modes it names feel relatable. If they don't, push back: "That doesn't match my experience. Here's what actually went wrong... "
Hold onto this list. You'll return to it in every lesson, and it'll look different each time you do.
Lesson reflection
- Which of your listed tasks felt "safe" to hand to AI, and which felt risky? Can you articulate why yet?
- What's one AI behavior you've noticed (good or bad) that you couldn't explain at the time?
What's next
Before the four properties, we need to draw a line around what "AI" means in this course. We're talking specifically about generative AI and how it's different from other forms of AI.
Feedback
As you progress through the course, we'd love to hear from you about how you are using concepts from the course in your work, plus any feedback you may have. Share your feedback here.
Acknowledgments and license
Copyright 2026 Anthropic. Original work building on the AI Fluency Framework developed by Prof. Rick Dakan (Ringling College of Art and Design) and Prof. Joseph Feller (University College Cork). Released under the CC BY-NC-SA 4. 0 license.