AI Pioneers · Course introduction
Forge's build, 20 min laterAI forProduct Managers
For product managers
Decide what AI should build.
Know whether it worked.
Sharper discovery, briefs that teams and coding agents can build from, and AI features with clear outcomes and honest evidence.
No coding required. Clear thinking and precise words.
Your teaching team
Three perspectives. One question each.
Whose problem are we solving, and what changes for them?
What must be true before anyone builds?
What does the evidence actually support?
Fictional personas. Real people direct, review and decide the content.
Who this is for
For people who decide what is worth building
Product managers & owners
whose teams use AI coding tools
PMs of AI features
responsible for features that use language models
Founders, delivery leads, designers
who write the briefs others build from
You bring
tickets, stories or briefs you've writtenno codingany AI assistantWhy now · the evidence
Teams build faster. They trust the result less.
The gap closes with
That's the product manager's territory.
Your turn
“We need a Stripe checkout button…”
Which statements belong in the problem brief?
Checkpoint · select everything that belongs in the brief, not in the design or the code
The method
From ticket to evidence
| Decision | In the brief | Evidence |
|---|---|---|
| Duplicate payments | A second payment never creates a second registration | Duplicates per month in the finance log |
| Course advisor quality | Suggests a fitting course for the visitor's stated role | 18 of 20 agreed evaluation cases |
| Advisor failure | When unsure, show the catalogue and offer a person | Fallback rate and complaint rate |
The learning path
Three weeks. Six modules. One capstone.
Week 1Decide and discover
01 The PM's role when AI writes the code
02 Discovery and research with AI
Week 2Brief and scope
03 Problem briefs teams and agents can build from
04 Scoping AI-powered features
Week 3Measure and launch
05 Measuring AI features
06 Responsible launch and alignment
AI-powered features
When the product itself uses AI
Assistants, summaries and recommendations behave differently from ordinary features.
Your job: set the quality bar, design the fallback, and decide when a person takes over.
I'm a QA engineer. Which course should I take?
AI-Assisted Quality Engineering looks like the best fit. Want to compare it with Spec-Driven Development?
- Quality bar
- Right course in 18 of 20 agreed cases
- Fallback
- Unsure? Show the catalogue and offer a person
- Cost
- Budget per conversation, reviewed monthly
- Never
- Promise jobs, prices or discounts
Your capstone
An AI course advisor, from one-line request to launch plan
Problem brief
Outcomes, non-goals and acceptance criteria
AI feature scope
Quality bar, fallbacks, cost and human handover
Evaluation and metric plan
Evaluation cases, success and guardrail metrics
Launch review
Risk register, transparency notice, stakeholder update
Your first step
Pick one ticket from your backlog
Capture it now. It stays on this device and waits for you in week 1.
Introduction complete
You have the right starting point
Your ticket and its open decision are saved on this device. Week 1 begins by auditing every decision a ticket leaves to the implementer.
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