AI Capabilities & Limitations: A companion course to AI Fluency & Foundations

Hi there. My name is Kristen and I'm on the education team at Anthropic. Welcome

to AI capabilities and limitations. If

you've taken our AI fluency framework and foundations course, you already know the four D's. Delegation, description,

discernment, and diligence. Those are

human competencies. They're what you do when you collaborate with AI. This

course is the companion piece. It's what

the machine does when a human prompts it and why.

We're going to help you build a working mental model of where AI outputs actually come from. Importantly, this

course centers on how AI is created, so it'll remain useful and relevant even as AI models and products change.

You can't delegate a task to AI without knowing where the model is strong and where it's weak. You can't discern the quality of an output from AI without some picture of how that output was produced and what to look out for.

Everything you learn in this course is actionable through the four D's.

Understanding that AI is a prediction engine changes how you describe tasks to it. Understanding the context window

it. Understanding the context window changes how you delegate long tasks. The

two frameworks fit together.

Here's the roadmap for the course.

First, we'll look at the two training stages that give an AI its character and the fingerprints each one leaves on the finished system. Pre-training and

finished system. Pre-training and fine-tuning.

Pre-training builds a document completer, while fine-tuning shapes AI into an assistant. Both stages matter for understanding why the model behaves the way it does.

And then, we'll cover four core properties of generative AI. Next token

prediction, knowledge, working memory, and steerability.

Each property is a continuum. You'll

learn to evaluate where your task falls along the continuum for each property, whether it's in the capability zone or drifting towards the edge.

Finally, we'll look at how everything interconnects. Most real-world failures

interconnects. Most real-world failures with AI are two properties meeting. A

hallucinated citation is next token prediction meeting a knowledge gap.

Drift over a long conversation is working memory meeting steerability.

When you can name the combination, you know why it's happening and what to do about it.

Models will keep changing. Context

windows grow. Hallucination rates drop.

New features ship every month. The

specifics shift constantly.

But the shape of these properties stays useful. Next token prediction will still

useful. Next token prediction will still be next token prediction a year from now. The knowledge cutoff might move,

now. The knowledge cutoff might move, but there will still be one. The context

window might get bigger, but it'll still be an edge.

What you're building here is a durable mental model, one that survives daily product updates and model releases.

As with all courses, the person doing the work is doing the learning. To get

the most out of this course, spend time doing the exercises.

The exercises are where the learning happens. We've designed them so you're

happens. We've designed them so you're testing each property against your own work in a domain where you're the expert. That's deliberate. You need to

expert. That's deliberate. You need to explore situations where you can actually tell if something's off. So

bring real tasks. Run real prompts. When

we cover working memory, load in your actual long documents. When we cover next token prediction, ask about your actual niche topics. The goal is a calibration you can feel, not a list of

terms you memorize. All right. I'm glad

you're on this learning journey with me and the rest of our team. Together,

we'll demystify how AI works and how you can use it to augment your thinking and your work.