Learning tracks by role

AI learning track: Product managers

For product managers defining AI features, acceptance criteria and the work people keep.

After this track: You can scope an AI feature, specify how to judge it and measure whether it improves the user’s work.

Follow these 10 steps in order, or return to the one you need for your current work. Reading times are estimates; listening times cover the full episode.

  1. How do you give an AI assistant a useful brief?

    Explainer · About 2 min reading

    Start by turning a vague feature idea into a task with evidence, constraints and an expected output.

  2. What is context in an AI agent?

    Explainer · About 2 min reading

    Specify the information the feature needs before blaming an incomplete answer on the model.

  3. Should you use prompting, RAG, or fine-tuning to customize an AI model?

    Explainer · About 2 min reading

    Use that information need to discuss retrieval, prompting or fine-tuning with your engineering team.

  4. What are AI evals, and what should leaders know about them?

    Explainer · About 4 min reading

    Turn the product promise into examples and acceptance criteria before comparing implementations.

  5. What is LLM-as-a-judge, and when can you trust it?

    Explainer · About 5 min reading

    Learn where automated judging helps so you can plan evaluation coverage without assuming every score is trustworthy.

  6. How much autonomy should you give an AI agent?

    Explainer · About 1 min reading

    Choose the feature’s scope of action before committing to an experience that promises to do everything for the user.

  7. What's the difference between human in the loop and human on the loop?

    Explainer · About 4 min reading

    Place approval and intervention points in the user journey once you know which actions carry risk.

  8. How do you measure whether AI is actually paying off?

    Explainer · About 3 min reading

    Measure completed work and remaining review effort before interpreting feature usage as customer value.

  9. AI Codes: Product Engineers Decide What to Build | Laurie Voss, Arize

    Episode · About 56 min listening · full episode

    Hear how product judgment changes when code is easier to generate so you can sharpen what the team chooses to build.

  10. 250,000 Lines of Code/Week: Inside an AMD VP's Agent-First Workflow | Anush Elangovan

    Episode · About 51 min listening · full episode

    Compare an agent-assisted coding workflow with your delivery process before promising a similar change in team capacity.

Choose another role or continue through the five-level pathway.