On-Demand Course
Designing and Prototyping Products with AI
Stop creating vibe debt with every prototype your team generates.
A 60-minute, self-paced course on eliminating vibe debt: the cost that accrues when you build prototypes that work in the lab but don't hold up when customers start using them. This course teaches you to write requirements an AI can't guess around, and helps you prove that your idea is feasible, affordable, and supportable before anyone commits to building it.
$199 • Self-paced • Instant access
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Your team can build almost anything now, but there's a price to unlimited prototyping.
With AI development tools, somebody can have an idea on Monday and there's a prototype running by Thursday. That's real productivity, it's new, and it feels like progress. Then it goes to engineering, and the feeling starts to change.
Most AI prototypes fail to reveal important details: whether the full product can be built at production scale on real data, what it costs per usage once actual customers are using it, and whether anyone can keep it running healthy a year from now when the model underneath it has moved and the rules the product depends on have changed.
To move past these experimental bottlenecks, you need a disciplined framework for governing AI prototyping. This course provides the exact methodology required to turn volatile prototypes into stable, production-ready products.
What You'll Learn
By the end of Designing and Prototyping Products with AI, you'll be able to:
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Write requirements an AI can't guess around.Hand an agent a line like "the app should let people know when something goes wrong" and it won't stop to ask what you meant. It fills the gap with something plausible and starts building. You'll rewrite loose intent as requirements specific enough that a machine and a team read them identically—and learn to spot the one that cant be made testable without an answer you don't have yet.
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Catch a bad architectural decision while it's still one sentence.Before the AI writes any code, it shows you its plan. You'll practice reading that plan critically and rejecting the step that's convenient and wrong.
3
Build one working slice through your riskiest path.We call this a steel thread—a single narrow slice that runs from the interface a person touches all the way down through the logic, the model or service it calls, the data, and the infrastructure it runs on, built on the architecture you actually intend to ship on.
4
Judge a product on cost and supportability, not just whether it runs.A slice that passes on feasibility alone is a demo. You'll test yours against the three questions reality demands—can it be built at production scale, does the value exceed what it costs to run, and can your team support it as models drift—and record an honest verdict with the weakest one named plainly.
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Who Should Take This Course
If you have validated your core concept but hit a wall asking "can this actually be built?", this course is tailored for you.
While project sponsors remain eager, engineering often remains hesitant, leaving you without concrete evidence either way. As weeks drag on, momentum stalls and key product decisions risk falling to whoever speaks loudest. You need tangible proof and definitive clarity to bring to the table.
If your most critical path relies on infrastructure owned by another team, this course is designed with you in mind.
Operating within an internal platform, enablement, or shared-services environment means navigating shared architecture while remaining accountable for stability and performance. Demonstrating system reliability in these settings presents distinct challenges, making proof all the more vital.
This isn’t for you if you’re looking to learn to code with AI.
This course does not cover how to evaluate, govern, or deploy live AI systems in full production environments. This course is deliberately designed to stop at a validated design and an MVP hypothesis.
$199 • Self-paced
Start the courseHow the course works
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Enrollto get instant access at $199. Start the course on your own schedule—no cohort, no deadlines, no time pressure.
2
Complete the courseby finishing four modules, about 60 minutes total, 40% of it hands-on. Every module hands you a hands-on exercise with a real prompt to run against whichever AI assistant you already use. Bring your own concept if you have one, or use the complete worked example built into the course. Across the four exercises you build a Behavioral Spec you download and keep, filled with your own work rather than a sample, backed by a working steel thread through your own riskiest path.
3
Apply it Monday morningby identifying the most critical architectural risk in a validated concept you are managing, then draft three concrete, testable requirements to address it. Map out a targeted slice through this path to demonstrate feasibility, cost-effectiveness, and maintainability. Present this risk assessment directly to your engineering team and document their feedback—developing this practical dialogue reflex is a primary goal of the course, actionable within 48 hours of completion.
AI product fluency is the new professional standard
Side-by-side in a single week, two teams present their work. Both show a functional display on screen, both reached this milestone rapidly, and both have good reason to take pride in their progress.
The critical distinction lies beneath the surface: one team can pinpoint their highest architectural risk, calculate the per-call cost under live customer load, and identify who owns ongoing reliability when underlying models shift. The other team simply has a working demo.
Inside the presentation room, the two appear identical. The difference only becomes apparent during production deployment—the absolute most costly time to discover it.
Stop leaving your product's stability to chance. Start building with the confidence that your AI prototypes are ready for the real world—and the teams that support them.
Self-paced • Bring your own Model • Earn a completion badge
Enroll Now—instant access for $199.png)