AI Product Management for New PMs
Learn what changes when your product's core behaviour is probabilistic, and how to manage it
A taste of a lesson
My manager says our AI classifier is 90 percent accurate. Is that good?
It depends on two things the number hides. First, what happens in the 10 percent? If it routes a support ticket to the wrong queue, someone moves it and loses a minute. If it marks a fraud case as safe, that's far more costly. Second, accuracy on what? A clean test set often looks better than messy real traffic. So ask: which mistakes does it make, how often, and how was it measured? Try one now: name the worst mistake your classifier could make and who would notice it.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain the core vocabulary of AI products in plain language to stakeholders
- Describe why AI features need evaluation sets instead of simple pass or fail tests
- Identify the main failure modes of an AI feature and plan for each
- Ask informed questions about cost, latency, data and quality in team meetings
- Write a one page brief for a first, human reviewed version of an AI feature
Lesson plan
- 1 What makes AI products different Understand probabilistic behaviour and why it changes how products are built and tested. Start
- 2 The vocabulary you will hear Use terms like prompt, context, retrieval, fine tuning and guardrails correctly. Start
- 3 Defining and testing quality Understand evaluation sets and write quality criteria for a feature. Start
- 4 Failure modes and the cost of being wrong Plan for confident errors, refusals, bias and outages before they reach users. Start
- 5 Cost, speed and data questions Know where cost and latency come from and what data questions to raise early. Start
- 6 Your first AI feature brief Write a one page brief for a modest, human reviewed first version. Start
Try asking
About this tutor
For new or aspiring product managers, and experienced PMs moving onto their first AI product. You do not need to code. You will learn the vocabulary your engineers use, why AI features behave differently from ordinary software, how quality is defined and tested, where cost and latency come from, and which failure modes to plan for. Each lesson ends with a small artefact, such as a one page feature brief or a list of failure cases, so you build a starter kit as you go. The goal is to ask good questions in your first AI product meetings and avoid the common beginner traps.
Reviews
4.3
4 ratingsSample
- Ji-woo P.Sample
I stopped nodding along when engineers said 'evals'. Now I ask what is in the eval set and whether it includes cases where the answer should be no.
- Lucas M.Sample
I moved from project management into a PM role on an AI search feature. The running help centre example made retrieval versus fine tuning finally make sense. I used the brief template in my second week.
- Fatima Z.Sample
Clear and patient. The lesson on error types was the best part. A bit basic in places if you already know some AI terms, but I liked being asked to explain things back.
- Owen R.Sample
Solid foundations, but I wanted more on working with data scientists day to day. The failure modes lesson was useful though.
About the teacher
Product management for AI features: deciding, specifying, testing and pricing them well
9 tutors 388 lessons taught Sample
I teach product managers and founders how to build AI features that people trust and keep using. I come from product work on software teams, where I learned that the hard part of an AI feature is rarely the model. It is deciding whether the feature should exist, writing down what good looks like, testing it before customers do and...
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