Most people's first experience with a generative AI system is a mix of delight and confusion. It produces a polished summary of a dense report in seconds, then confidently invents a citation that doesn't exist. It follows a detailed instruction perfectly, then ignores a simple one in the very next message. Without a mental model of what's happening underneath, these moments feel random — and it's hard to know whether to trust the next output, or how to fix the last one.

This course gives learners that mental model. It's the companion to AI Fluency: Framework & Foundations: where that course teaches the human competencies (Delegation, Description, Discernment, Diligence), this one teaches the machine properties those competencies are responding to. The two are designed to be taken in either order, and together they form a complete picture of effective human-AI collaboration.

We organize the course around four properties that shape what an AI system can and can't do for you: Next Token Prediction (where AI answers come from), Knowledge (what the model actually knows, and why it can be confidently wrong), Working Memory (what it's paying attention to right now, and what falls off the edge), and Steerability (how much control your instructions really give you). Each property sits on a spectrum from capability to limitation, and each section pairs a short explanation with a hands-on exercise so you can feel where the edges are rather than just read about them.

The final section looks at what happens when these properties collide — because in real use, they always do. A long document pushes against working memory while also straying into knowledge the model doesn't have; a vague instruction tests steerability at the same moment next-token prediction is reaching for whatever sounds most plausible. We close with a practical diagnostic: how to look at an unexpected output, recognize which kind of unexpected it is, locate roughly where on the capability-to-limitation continuum your task landed, and respond with a targeted fix instead of a generic retry.

Recommended prerequisites

None. This course assumes no technical background and no prior experience with AI tools. If you've already completed AI Fluency: Framework & Foundations, you'll recognize where each property connects to the 4Ds — but it's not required.

Who this is for

Anyone who uses, or is about to start using, generative AI in their work or studies and wants to understand why it behaves the way it does. Educators, students, knowledge workers, and team leads will all find the same core model useful, because the properties it describes don't change across use cases.

Claude Code setup