AI Foundations for Product Managers
Make product decisions with a working understanding of models, retrieval, agents and evaluation. Build a feature brief with test cases, economics and recovery design, then practice explaining your choices. Includes a transition route for engineers and data scientists.
Opening lesson: Answer whether and for whom before you answer how
Syllabus
Open a module to choose a lesson. Access is shown beside each title.
01Change the question3 lessons · 44 min
Move from how a system works to whether it should exist: estimate value before effort, turn estimates into bets with reversal conditions, and present your engineering past as product evidence.
02Understand model behavior4 lessons · ~1.5 hours
Choose the information and model behavior a product needs, then test the parts that can fail.
03Own the user and the number3 lessons · 50 min
Find the user problem before the model, choose the metric a model change should move, and write specifications that engineers and coding agents can build from without guessing.
04Build the evidence5 lessons · ~1.5 hours
Inspect failures, calibrate judgment, design recovery and calculate whether a feature deserves a launch.
05Build and defend a feature3 lessons · ~3.5 hours
Bound a first agent, verify research and assemble the evidence needed to approve a product experiment.
06Lead without the keyboard4 lessons · ~3.5 hours
Make calls with incomplete evidence, say no to technically interesting work, answer product sense questions from the product seat, and finish with a capstone that rebuilds engineering answers as product answers.
About this course
You can know how a model works and still choose the wrong product. This course puts technical understanding beside the decisions it should improve: what to build, what evidence to require, what it costs and where the system should stop.
The recurring case is Kitewire, a fictional logistics company serving retailers. You decide whether to predict delays, design a support assistant and defend its launch. All case figures are teaching assumptions. The final mock switches to Railnote so you have to transfer the method.
Choose your starting route
If you are new to AI, work through the whole course. The opening section establishes product reasoning; Understand model behavior explains the technical mechanisms without assuming engineering experience. The examples then become a feature specification, an evaluation and a business case.
If you already build AI systems, test yourself on the free model-behavior lesson before deciding what to skim. Keep the product discovery, experiment-design and leadership exercises. Knowing how to implement a system does not answer whether it deserves investment.
What you will produce
Keep one working notebook. By the end, it should contain a product bet, a context contract, a retrieval diagnostic, a labelled evaluation set, a judge confusion table, a cost worksheet and a risk register. The three-hour applied project combines these into a delivery-support feature brief. The closing mock practices the decisions on a fresh rail-ticketing case.
Every lesson has a diagram, an exercise with reference reasoning and a short self-check. Attempt the exercise before reading its reference. Coding and paid model access are optional; paper traces and supplied fictional records let you do the core work.
A pace you can sustain
| Plan | Suggested sequence |
|---|---|
| Four weeks | Product framing and models; user/specification; evidence; project and transition mock |
| Eight weeks | One section each week, with separate weeks for project revision and the final mock |
| Twelve weeks | Split longer sections across weeks and repeat weak exercises on a different product |
The time shown in the syllabus includes suggested exercise and project time. It is an estimate, not a completion guarantee. A useful pace leaves time to revise an answer after feedback.
Two lessons are public and two more are available with a free account. Premium includes the remaining lessons and projects. Assessments are for self-review; the course does not issue a qualification.
When you finish, you can
- Choose a useful AI intervention and compare it with a credible non-AI baseline.
- Write a context contract, diagnose retrieval failures and defend a model-adaptation choice.
- Build evaluation cases, calibrate a judge and calculate operating cost including human work.
- Defend a delivery-support feature with evidence, then translate technical experience into product interview answers.
BEFORE YOU START · Comfort describing a user problem and working with percentages. No model-training or coding experience required. Engineers can use the opening and closing sections to practice the transition into PM work.