Set the boundary before AI touches real work: decide what AI should do, make it safe, give it context, and know when it's wrong.

The wrong AI boundary creates problems downstream. A privacy gap here follows you into every workflow that comes after it. A task mis-bucketed as “AI-suited” costs you trust the first time it's wrong.

Turn uncertainty into a confident, defensible product decision, research with evidence instead of assumptions, and decide what's worth building before investing time.

AI makes research faster, not more real. It accelerates synthesis and documentation, but it doesn't decide what's worth building, trusting the verdict is still your job. The goal is faster confidence, not faster guessing.

Get AI to argue with itself before you commit: three radically different directions for the same problem, then AI as critic, arguing against your own choice.

The first idea a model gives you is the average of every similar idea it's seen. Sparring and a critic pass are what surface the option you'd have missed otherwise.

Decide, screen by screen, whether to stay in Figma, go hybrid, or leave for code, and follow through on that decision instead of defaulting to whatever's easiest.

Most teams either over-build static Figma screens that never needed interactivity, or under-build and only discover missing auth, state, or API needs after handoff. This is where you catch that early.

Take the hybrid or leave-for-code path into a real build: a two-tier entry, an AI build pass, and a break/fix loop, with a clear path for when you're stuck.

This is where "looks done" becomes "actually runs." Skipping the break check is how AI-generated code ships with failures no one caught.

Run usability testing, an accessibility sweep, interaction analytics, trust design, and a bias audit on what you built; then decide whether the evidence actually changes anything.

Collecting evidence isn't the same as acting on it. The real decision is the revise-or-proceed gate; get it wrong and you patch a screen that needed a rebuild, or rebuild something that just needed a patch.

Sync tokens and Code Connect, deploy to a CI/CD sandbox, benchmark velocity against quality, and decide; with engineering; whether it’s ready, on hold, or escalated.

This is where speed either holds up under real release pressure or quietly erodes quality. The engineering-ready and ship/hold/escalate gates keep that call from being yours alone.

Close the loop: reflect on the entire cycle you just ran, then write your own Personal AI Practice Plan for what you'll keep doing after the course ends.

A course only proves what it teaches when you've run the whole loop once yourself, then decided, in writing, what you're actually taking back to your team.