What you'll learn

Estimated time: 45 minutes

By the end of this lesson you'll be able to:

• Introduce the builder's toolkit and where AI adds value at each stage
  
• Explain why delegating implementation is safe and delegating judgment is not
• Write acceptance tests that define done before a single line of code exists

Delegation & the builder's toolkit

(5 minutes)

Before you write a single line of code, you make a dozen decisions that shape whether what you build will matter. This lesson reframes delegation: it’s not “should I use AI here?” but “I have a customer problem — how do I break it down, and what role does AI play?”

Key takeaways

• Delegation means decomposing the problem first, then deciding what AI handles at each step.
• Write acceptance tests before code. They give you and AI a shared definition of done.
• As AI accelerates implementation, your value shifts to framing problems and raising the bar.

Exercise

The Clinic Wait Time Project, Part 1

This project threads through the entire course. You’ll return to it in every lesson, and by the end you’ll have built something real. But in this lesson, you write zero code. That’s intentional.

The scenario: A community health clinic needs a way for patients to check wait times before coming in. That’s all you’ve been told.

Your job this session is to do the work that comes before building. You’ll produce three deliverables.

Deliverable 1: A Problem Brief (one page max)

Answer these questions in plain language:

• Who are the users? Be specific. Patients, yes — but which patients? What about staff? Who else touches this?
• What do they actually need? Not “a wait time checker.” What outcome are they hoping for?
• What does a great solution feel like to use? Describe the experience, not the features.
• What are the real constraints? Think budget, technical skill at the clinic, patient access to devices, privacy requirements.

Deliverable 2: A Delegation Plan

Map the build to the six toolkit capabilities. For each one, decide which AI collaboration mode fits best: Automation (AI does it, you check), Augmentation (you and AI work it together), or Agency (AI operates with latitude inside boundaries you set). Make a simple table — six rows, one per capability.

Deliverable 3: Acceptance Tests

Write five to seven statements that define “done.” Each must be concrete enough that a stranger could tell you whether the finished product passes or fails.

Good: “A patient with a basic smartphone can find the current wait time in under 30 seconds without creating an account.”
Bad: “The system is easy to use.”

Lesson reflection

• Where did you spend most of your time across the six capabilities on a past project? Where should you have spent more?
• If implementation became effectively free tomorrow, where would your value as a builder come from?

What's next

In the next lesson, we move from delegation to description. You’ve decided what to build and who’s doing what — now you need to communicate that clearly enough that AI can actually execute it.

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. This course was developed in partnership with CodePath, 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.