Build the skills to make better AI product decisions.

Structured learning pathways for product managers transitioning to AI product work, built around the decisions you actually have to make.

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How it works
01
Pick a pathway

Choose the AI product skill or decision area you need to strengthen. Each pathway is a curated sequence, not a library you browse.

02
Work through practical lessons

Learn through focused explanations, real examples, and scenarios that reflect the decisions PMs actually face. Not generic AI theory.

03
Apply what you learn

Each lesson ends with a framework, template, or artefact you can use in your actual product work, not just something you have read.

Pathways

Two pathways, built for different starting points.

Beginner friendly

AI-Powered PM

Build an AI-augmented workflow for the core PM job: research, prioritisation, PRDs, stakeholder communication, and responsible delivery.

  • AI-assisted research and synthesis
  • Prioritisation frameworks for AI feature decisions
  • Writing PRDs and specs with AI in the loop
  • Responsible delivery and stakeholder communication
Coming soon

The AI Product Manager

The full process for a PM transitioning to AI product work, covering scoping and architecture through evaluation, UX, launch, and governance.

  • Scoping and evaluating AI opportunities
  • AI architecture decisions without an engineering degree
  • Evaluation, quality, and failure modes
  • Launch, UX, and governance for AI products
Learn something. Leave with something.

Every lesson ends with a usable artefact.

Not notes. Not a certificate. A template, framework, or specification you can open in your next meeting. Claryfy is built around the idea that learning should produce work, not just understanding.

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ArtefactLesson 5
Opportunity Brief Template
Problem statement
Mid-market users struggle to align stakeholders on AI feature scope before sprint planning, which leads to rework after engineering has started.
Why now
Three AI features being scoped in parallel with no shared framework. Engineering cycle starts in 6 weeks.
Hypothesis
We believe a consistent scoping process will reduce mid-sprint scope changes by 40% in Q3. We will know this is true when…
Example output. Your artefacts are filled with your own work.
Pricing

Free to start.

Create an account and start learning. No credit card required. You can complete lessons and access artefacts without paying anything.

Ready to make better AI product decisions?

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Start free

Free to start · No credit card required