The AI course for product managers who want to ship the prototype themselves.
Most AI courses for PMs teach prompting and a strategy framework. That covers about a week of the job. The harder parts come after: turning a feature idea into something a user can click, deciding whether model output is good enough to ship, and getting a pile of feedback into a shape you can reason about.
This page lays out what a good course should cover, what you should be able to build by the end, and how to compare options. Our 2-week in-person program in Austin is one of them.
Six skills a product manager should leave with.
Each one maps to a day in our curriculum, so you can hold any other course to the same list.
How models actually behave
Where Claude, GPT and Gemini differ, where they make things up, and why the same prompt gives different answers. You will be writing specs for AI features, so you need a feel for what the model can carry.
A PRD a builder can work from
Scoped tightly enough that Claude Code can build from it and clear enough that an engineer can review it. Scope before you build.
From idea to a live URL
Describe a page in plain English, build it with Claude Code, push to GitHub, deploy on Vercel. By the afternoon you have a link you can send to five users.
Real data behind the prototype
A Supabase table, a form, a dashboard, and logins with row-level security. User tests run on something that works instead of a mockup.
AI wired into your tools
Composio connects Claude to Gmail, Slack, HubSpot and 200+ other tools without integration code. Your feedback, tickets and notes stop living in five places.
Evals
How to test whether an AI feature is good enough to ship before engineering spends a sprint on it. Plenty of PM courses skip this. It is the skill your engineers will respect most.
Three builds that fit a PM’s week.
Examples scoped to what the curriculum covers. In the program you pick your own, and it runs on your product’s data.
Feedback triage agent
Pulls support emails and Slack threads, has Claude tag each one by theme and severity, and writes the rows to a database. A dashboard shows which themes grew this week and links back to the source messages.
A feature prototype with real data
Your next feature idea, built from your PRD with Claude Code and deployed with a real database behind it. You run the user test on Thursday instead of waiting for a sprint slot.
Competitor change monitor
A browser script visits competitor pricing and changelog pages on a schedule. Claude summarizes what changed, and a digest lands in Slack before your Monday planning meeting.
Illustrative examples, not alumni projects. See what graduates actually built.
How to choose an AI course as a product manager.
- Ask where the final project lives.If the answer is a slide deck or a notebook, you are buying a strategy course. That can be fine. Just know which one you are paying for.
- Check whether you work on your own data.Toy datasets teach the tool. Your own tickets, docs and metrics teach you where it breaks, which is the part you need at work.
- Look for evals and data handling on the syllabus.If you will put AI in front of customers, you need both. A course that skips them leaves you dependent on engineering for every quality call.
- Count the hours with an instructor.Recorded video is cheap to produce. Live help when your deploy fails is what costs money, and it is usually what gets people unstuck.
- Be honest about your time.A self-paced online course costs less and fits around a job. It works well if you are self-driven and need one specific skill. It tends to stall the first time something breaks and nobody is there to ask.
One case where we are the wrong pick: if your job is managing a team that trains its own machine-learning models, look for an AI product management course built around data science workflows. Ours is about building products on top of existing models like Claude.
Two weeks in Austin, building on your own product.
Our program is a full-time, in-person cohort for people who know their domain and want to build with AI themselves. PMs fit it well, because writing the spec is already half the job.
- 2 weeks, Monday to Friday, 9am to 3pm, in person in Austin
- $3,999 tuition, paid in full ($3,499 with the scholarship for veterans, first responders and educators)
- Next cohort starts January 11, 2027; enrollment closes January 4
- Small cohort, with an instructor in the room all day
- Ship a live website, a working AI agent and an automated workflow, or a full refund
- Demo Day on the final Friday, in front of Austin’s tech community
AI courses for product managers, answered.
Do product managers need to code to take an AI course?
No. You will work in a terminal with Claude Code from the first week, but you describe what you want in plain English and review what it writes. Knowing your product and users matters more than syntax.
How long does an AI course for product managers take?
Ours is two weeks, full time: ten days, 9am to 3pm. If two weeks away is not realistic, our 3-day workshops cover a shorter version of the build path.
How much does it cost?
Tuition is $3,999, paid in full. Veterans, first responders and educators pay $3,499 with the scholarship. Training for a whole product team is quoted separately on our corporate page.
Will this change how I work with engineers?
It usually does. After you have deployed to Vercel, wired up a Supabase database and tested an agent yourself, your specs get tighter and you can tell a one-day change from a one-month one.
Cohort 3 starts Jan 11.
Bring the feature you keep waiting on.
Book a short call and tell us what you want to build. We will tell you honestly whether two weeks gets you there.