AI Product Management
8 weeks
AI opportunities, LLM fundamentals, use-case design, prototyping, RAG, evaluation, responsible AI, governance and launch.
- Duration
- 8 weeks
- Level
- All levels
- Format
- Live online, delivered in weekly cohort sessions.
- Status
- Planning
Who it's for
- Product managers adding AI to their toolkit
- Founders exploring an AI-enabled product
- Technical PMs and delivery leads working with AI features
- Anyone deciding whether an AI opportunity is worth pursuing
What this course covers
AI opportunities, LLM fundamentals, use-case design, prototyping, RAG, evaluation, responsible AI, governance and launch.
What you'll be able to do by the end
- Map and rank real AI opportunities
- Explain core LLM concepts without needing a technical background
- Design a specific, testable AI use case
- Build and test a working prototype
- Understand when and how RAG fits a product
- Evaluate an AI feature's quality and reliability
- Apply responsible AI and governance practices
- Plan a credible launch for an AI feature
Syllabus
Week 1: AI opportunity mapping
- Where AI genuinely adds product value, and where it doesn't
- Mapping opportunities against workflow, value and risk
- Ranking opportunities instead of chasing every idea
- Choosing one opportunity worth pursuing further
Week 2: LLM fundamentals
- How large language models actually work, in plain terms
- Capabilities, limitations and common failure modes
- Reading model behaviour without a technical background
- Vocabulary every product person working with AI needs
Week 3: Use-case design
- Turning an opportunity into a specific, testable use case
- Defining success before writing a single prompt
- Scoping a use case a small team can actually build
- Common use-case design mistakes
Week 4: Prototyping
- Building a working prototype fast, without over-investing
- Prompting as a product design activity
- Getting useful feedback from an early prototype
- Deciding what the prototype needs to prove
Week 5: Retrieval-augmented generation (RAG)
- What RAG is and when it's actually needed
- Data, retrieval and grounding in plain terms
- Common RAG failure modes and how to test for them
- Deciding build, buy or wait for a RAG-based feature
Week 6: Evaluation
- What “good” looks like for an AI feature
- Building a lightweight evaluation set
- Testing for hallucination, bias and edge cases
- Turning evaluation results into product decisions
Week 7: Responsible AI and governance
- Trust, transparency and human oversight
- Data, privacy and risk considerations
- Setting guardrails and release gates
- Building accountability into an AI feature
Week 8: Launch
- Preparing an AI feature for real users
- Communicating what the feature can and can't do
- Planning for monitoring and iteration after launch
- Final project: a launch-ready AI opportunity brief
How the course works
- Live online, delivered in weekly cohort sessions.
- A 2-hour live session each week.
- 1 to 2 hours of self-study between sessions.
- Ongoing support between sessions, plus feedback on practical work.
Practical work
Across the course you'll produce real, usable work, not just notes. You'll leave with:
- An AI opportunity map and ranking
- A working prototype of one AI use case
- A lightweight evaluation set and results summary
- A final launch-readiness brief covering risk, governance and rollout plan
What's included
- A reusable AI opportunity-mapping template
- Templates and worksheets for every module
- Recordings of every live session
- Slides from each session
- Feedback on practical work
- Certificate of completion
Prerequisites
No coding or machine learning background required. This is a product and judgement course, not a technical AI engineering course.
Who delivers it
Collins Obasuyi, Founder & Principal Consultant
Collins works across product strategy, AI, delivery, quality and education, and teaches from real product and delivery work rather than abstract theory. Sessions are built around practical judgement: making decisions, working through trade-offs and applying methods that hold up outside the classroom.
FAQ
Do I need a technical or engineering background?
No. The course focuses on product judgement, not model-building. Concepts are explained without assuming prior AI or coding experience.
Will I actually build something?
Yes. You'll build and test a working prototype of one real AI use case over the course.
Are sessions recorded?
Yes. Every live session is recorded and shared with participants who can't attend live.
Can my employer pay for this course?
Yes. We're happy to provide an invoice for employer-funded enrolment.
Start a conversation
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