Choose this when
You are deciding what to build, how it should feel, and whether it is good enough to ship.
All learning paths
AI learning path
AI product work
Make better calls about which AI features deserve to exist and how to measure them.
- Best for
- PMs, designers, founders
- Level
- Beginner to intermediate
- Time
- 8-12 hours
You should be able to
You can turn an AI idea into a use case, prototype, UX risks, quality bar, and eval plan.
Checkpoint
Move on when you can say what the feature should refuse, ask, or escalate.
Do
Learning sequence
Work through the material inside each step. Videos are embedded where they fit; tutorials and references sit next to the task they support.
Step 1
Choose the use case
Score ideas by user pain, data availability, reversibility, and cost of mistakes.
Watch here
►
AI Powered Product Skills for Executives and GMs
Frames AI product judgment around use cases and business outcomes.
Open here
Lenny's Podcast: AI product episodes
You want AI product examples from PMs and founders.
Open resourceStep 2
Prototype the loop
Design the interaction, feedback, and correction points around imperfect output.
Watch here
►
How a Meta PM ships products with AI
Use this while thinking through prototypes and product loops.
Open here
AI Evals for Engineers & PMs
You are shipping AI features and need a serious evaluation workflow.
Open resourceStep 3
Measure quality
Define examples, evals, human review, and launch thresholds before release.
Open here
The AI Engineer Roadmap
You want a guided path through core AI concepts, model selection, the AI engineering mindset, evals, and techniques for improving LLM-powered apps.
Open resourceLLM Evals
Your AI app needs quality checks before users see it.
Open resourcePractice task
Write a one-page AI feature brief with target user, non-goals, failure modes, and five example eval cases.
Reference
All resources in this path
Step 1
Lenny's Podcast: AI product episodes
You want AI product examples from PMs and founders.
Step 2
AI Evals for Engineers & PMs
You are shipping AI features and need a serious evaluation workflow.
Step 3
The AI Engineer Roadmap
You want a guided path through core AI concepts, model selection, the AI engineering mindset, evals, and techniques for improving LLM-powered apps.
Step 3
LLM Evals
Your AI app needs quality checks before users see it.
Educators to follow
Peter Yang
Intermediate
Review the Maven syllabus and compare it to your current product workflow.
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Lenny Rachitsky
Beginner to intermediate
Browse the How I AI interviews and copy the workflows that match your role.
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Shreya Shankar
Intermediate
Review the course outcomes and pair it with a real feature you can evaluate.
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Hamel Husain
Intermediate to advanced
Read the evals guide and build a small test set for your own app.
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Josh Pigford
Beginner to intermediate
Read the public notes and examples before deciding whether the paid material matches your business.
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Brian Casel
Beginner to intermediate
Look for workflow breakdowns and implementation examples.
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