Business Free course · Certificate included
AI Product Management: From Idea to Reliable AI Features
Strategy, autonomy, AI UX, PRDs with evals, prototyping, unit economics, pricing, launch, impact, regulation and team design
- Intermediate
- 7 h 2 min
- 16 lessons in 6 modules
- 2 h 17 min of video lectures
- Updated Sep 2026
About this course
AI features fail for product reasons far more often than model reasons: the wrong problem, the wrong level of autonomy, no definition of good, runaway costs or a launch nobody could measure. This intermediate course teaches product managers, founders and team leads to take AI features from idea to reliable production. You will pick problems where AI creates value, set autonomy levels by error cost and reversibility, and design UX for uncertainty with streaming, citations, undo and human handoff. You will write PRDs with buildable evaluation criteria and golden sets, prototype behaviour in two days, and decide between buying, APIs and open weights. You will turn tokens into cost per task, design pricing that protects margin, run evals-driven development, launch in stages with safety reviews, and measure real impact. Finally you will map 2026 regulatory touchpoints, organise your team, and complete a decision-ready capstone.
Tools you’ll use
- OpenAI Playground
- Anthropic Console
- Google AI Studio
- n8n
- Zapier
- promptfoo
- Google Sheets
- OpenAI Python SDK
Skills
- AI product strategy
- AI UX design
- PRD writing
- LLM evaluation
- AI unit economics
- AI pricing
- AI governance
- Product analytics
What you’ll be able to do
- Prioritise AI opportunities by fit, value net of review and risk
- Set autonomy levels per action and design UX for uncertainty and handoff
- Write AI PRDs with golden sets and buildable evaluation thresholds
- Prototype AI behaviour quickly and choose between buy, API and open weights
- Model cost per task and design AI pricing that protects margin
- Run evals-driven development, staged launches and honest impact measurement
- Map AI regulatory touchpoints and organise teams to ship AI reliably
Curriculum
Syllabus
- Modules
- 6
- Lessons
- 16
- Reading time
- 4 h
- Assessment questions
- 25
Where AI creates value, how much autonomy to give it, and how to build defensible AI products amid fast model and platform change.
- Where AI creates value (and where it does not)Video lecture, 9′14 min
- Automation vs augmentation: choosing the level of autonomyVideo lecture, 8′14 min
- AI product strategy: differentiation and defensibilityVideo lecture, 8′14 min
Interaction patterns that make AI checkable, fixable and controllable, and a systematic approach to failure, safety and calibrated trust.
- UX patterns for AI: streaming, citations, confidence, undo and handoffVideo lecture, 8′15 min
- Designing for failure, safety and calibrated trustVideo lecture, 8′15 min
Write AI PRDs with buildable evaluation criteria, prototype behaviour with playgrounds and no-code tools, and choose between buying, APIs and open weights.
- Writing AI PRDs with evaluation criteriaVideo lecture, 9′16 min
- Prototyping AI features with playgrounds and no-code toolsVideo lecture, 8′15 min
- Build, buy or API: data and model choices for PMsVideo lecture, 8′15 min
Translate tokens into cost per task and margin, find the real cost drivers, and design AI pricing that captures value while protecting margin.
- Unit economics: from tokens to cost per taskVideo lecture, 9′16 min
- Pricing AI features: seats, usage, credits and outcomesVideo lecture, 9′15 min
Run evals-driven development with engineering, launch through staged rollouts and safety reviews, and measure quality, adoption, outcomes and cost honestly.
- Evals-driven development with your engineering teamVideo lecture, 9′16 min
- Launch, staged rollout and safety reviewsVideo lecture, 9′15 min
- Measuring impact: quality, adoption, retention and costVideo lecture, 8′15 min
Regulatory touchpoints for AI PMs as of 2026, how to organise teams and ways of working for AI, and a decision-ready capstone package.
- AI regulation touchpoints for product managersVideo lecture, 9′16 min
- Organising for AI: roles, team models and ways of workingVideo lecture, 9′14 min
- Capstone: an AI feature PRD, eval plan and business caseVideo lecture, 9′20 min
- Final assessment
Your certificate
Finish with a credential anyone can check
Earn the Certified AI Product Manager badge: The holder can prioritise AI opportunities, set autonomy levels, design trustworthy AI UX, write PRDs with golden sets and buildable evaluation thresholds, prototype quickly, choose between buying, APIs and open weights, model cost per task and pricing, run evals-driven development and staged launches, measure impact honestly, and navigate AI regulation and team design.
Completed all lessons and scored at least 80% on the final assessment.
- A public verification page
- A PDF certificate to download
- An Open Badge you can share
- One click to your LinkedIn profile
Final assessment
- 25questions drawn from a larger pool
- 40 mintime limit
- 80%pass mark
- 3attempts per 24 hours
Start learning today. It’s free.
Every lesson is free to read. A free account saves your progress, unlocks the final assessment and issues your certificate.