Introducing Designing and Prototyping Products with AI, our latest 60-minute self-paced course on one of the most critical phases in AI product development: the gap between knowing what people want and knowing that you can actually build it.
It's live today on SAFe Studio™.
Why a demo stopped being proof
Generating a prototype used to be the expensive part, and that expense is what made a demo mean something. You could show the thing running, and you proved that somebody on the team could do the hard work.
But generative AI has ended that. Demo production is simply and nearly free. Your team can produce something that runs in an afternoon, and most teams do, repeatedly.
But there's a cost that nobody is pricing in. Every one of those prototypes is debt, and somebody eventually has to pay up. We like to call it vibe debt, the distance between something that runs and something that holds up against real customer interaction.
What the course is
Product prototyping has moved from we created a thing to now building a vertical slice of your product all the way from the first customer interaction down through your real architecture. The aim is to prove the product is feasible, affordable, and supportable well beyond a simple prototype. In this class you leave with two things:
- A behavioral spec: requirements precise enough that your team and an AI can read them the same exact way.
- A steel thread: one working slice through the riskiest path of your real product system from the interface a person touches down through the logic the model the data and the infrastructure the product runs on.
What's inside:
- Turn loose intent into requirements an AI can't guess around. You set the rules your build can't break, then rewrite vague "the app should" statements as requirements that are specific and testable using the Easy Approach to Requirements Syntax (EARS).
- Build one working slice through your riskiest product path. You build narrow, deep, and on the architecture you intend to ship on. The move that matters is reviewing the AI's plan before it writes a line of code.
- Prove it can exist and persist. You judge your steel thread and product hypothesis on three questions: can it be built at production scale? Does the value exceed what it costs to run? And can your team support it as models eventually drift?
- State a minimum viable product hypothesis your evidence supports. Name what the first release needs to prove, and stop there. Build that product.
Who it's for
This course is best suited for product managers and designers who've validated a concept and hit a wall at "can we actually build this?" They have sponsors who are convinced, engineers who might be uneasy, and nobody has evidence to express who is right or wrong. So the decision goes to whoever argues hardest in the room.
It's also for anyone who's built something with AI that demos beautifully and isn't sure it's real. You can build prototypes but you can't prove value.
Internal platform and enablement teams get as much out of this as commercial ones do. If your riskiest path runs through a system you don't own, proving it holds is harder, and it matters more.
How it works
The course is self-paced and on-demand, and includes 4 modules of about 12 minutes each, plus a short opening and close, so roughly an hour end to end. Every module has a hands-on exercise you run against whichever AI tool you already use. Start whenever, move at your own speed, and revisit any lesson for a refresher.
Enrollment is $199 for instant access. No cohort, no deadlines, no prerequisites. You finish with your Behavioral Spec, a working steel thread, and a completion badge.
Questions you might have
Is this for me if I'm not technical? Yes, and arguably more so. You're the reviewer here, not the builder. In the core exercise, an AI proposes a build plan with a step buried in it that ships user location data to an outside service. You catch it because it breaks a rule your build wasn't allowed to break. No code written, none read.
Do I need to take AI-Empowered Product Discovery first? No. The course opens by handing you a completed, validated brief to work from, so you never start from a blank page. Discovery pairs well with this one, but it isn't a prerequisite.
Do I need my own product to work on? No. Every exercise runs on Apex, a hypothetical companion app for a premium urban e-bike company whose riders need to know where it's safe and legal to charge. Bring your own work if you have it, and use Apex as the worked example alongside the course material.
How long does it take? About 60 minutes. Each module ends with a piece of your spec saved, so you can stop after any one of them and pick up later without losing progress.
Will this be obsolete when the tools change? There's no tool here to go obsolete. The course teaches no vendor stack by design. Writing intent an AI can't guess around, reviewing a plan before code exists, judging a slice on what it costs and who has to support it: none of that expires when you switch models or platforms.
Start with the hardest part
You can generate a prototype in an afternoon. This is the course about proving it will hold.
