Your product launch used to be the finish line. With AI, it’s your first step.
A 60-minute, self-paced course on what has to happen after you ship: scoping a release small enough to prove its value, running the build-measure-learn loop yourself, and governing your spec so each release compounds instead of slowly losing value.
The product you shipped often isn’t the product you have.
Most product teams move on after launch, leaving success metrics to siloed dashboards that offer insights too late to be actionable. Without active governance, “quick” code patches replace proper spec updates, leading to a dangerous drift where your documentation no longer reflects the actual product.
Closing this gap requires more than just better tracking; it requires a fundamental shift in how you govern AI behavior. This course provides the framework to reclaim control over your product’s evolution.
What you’ll learn
Scope a release small enough to prove your claim.
First releases usually miss in one of two directions: too bare for anyone to notice the value, or so loaded with negotiated features that the budget’s gone before you learn anything. You’ll find the setting between them and ship the smallest thing that gives your claim a fair test.
Write acceptance criteria a machine can check.
“It works well” isn’t a standard when behavior is probabilistic. You’ll write criteria as measurable system behavior: what it must do, how reliably, and within what bounds. Precise enough that an automated check can score a real production run against it.
Measure what matters for your product, internal or external.
Adoption, retention, and commercial outcome if you’re building for customers. Workflow penetration, productivity, and cost if you’re building for your own organization. Most AI product training assumes the first one and never says so.
Govern changes so improvements compound instead of cancel out.
Turn the criteria you wrote into eval gates that catch a regression before it reaches customers, keep your spec and your live product in agreement, and make every change traceable to the version it was built against.
Who should take this course
This is for you if you shipped an AI feature and can’t say whether it’s working.
You’ve got a dashboard or a monthly readout, and neither one tells you whether the thing is delivering the value you promised when you pitched it. You want evidence you can act on to continuously improve your product for users.
This is for you if your team fixes AI behavior in the code because it’s faster.
You already know the spec and the live product have drifted apart. Changes keep stacking on documentation that is no longer accurate. You don’t need convincing that it’s a problem, but you do need a way to stop it.
This isn’t for you if you haven’t shipped anything yet.
The course assumes you already know build-measure-learn and teaches you to own and govern it. If you’re looking for an introduction to the loop, start elsewhere. Same goes if you’re shopping for a tooling stack as this course teaches you to run a governed product loop, not which vendors to buy.
How the course works
Enroll to get instant access at $149.
Start on your own schedule — no cohort, no deadlines, no time pressure.
Complete the course
by finishing four lessons, about 60 minutes total, 40% of it hands-on. Every module gives you an exercise to run in your own AI assistant, with a real prompt. Bring your own product materials if you have them, or use the running example built into the course — it works either way. Across the four exercises you build a spec scaffold you download and keep, filled with your own work rather than a sample.
Apply it Monday morning
by taking one AI capability you’ve shipped or are about to ship. Name the one outcome metric that would prove it’s delivering real value. Then write one acceptance criterion for it precise enough to become an automated eval gate: what the behavior must do, how reliably, and within what bounds. That’s the reflex the whole course is built to give you, and you can run it on a live product within 48 hours of finishing.
AI product fluency is the new professional standard
Consider a pair of products twelve months post-launch, both developed by teams of identical caliber starting from the exact same position.
For the first product, every alteration was funneled through an exhaustive product specification first, ensuring the documentation continuously mirrored the live environment and permitted subsequent updates to build cleanly on previous versions. Conversely, the other product bypassed specifications for direct coding in the interest of immediate speed. While this approach led to no major disasters, its overall value quietly eroded.
Despite utilizing identical skill sets and effort, the defining difference was that one team managed the feedback loop while the other failed to do so.
A successful launch is a solid achievement, yet it represents only the opening move.
This course creates the discipline required to transform a simple launch into an enduringly profitable product: an ongoing value engine for the product’s entire lifecycle, alongside a structured specification you can immediately implement in your day-to-day operations.
