Free course · about 5 minutes to learn · Product Management
Become AI‑native at Product Management in five minutes
Learn the move strong AI-native product managers make first: turning an ambiguous request into something an AI system can actually build, test and be judged against.
- About 5 minutes of lessons, then a short exercise you write
- Free, no card
- Human-reviewed credential
- No account to start
What you will be able to write
- In
- What the feature receives, and what it must refuse.
- Out
- The behaviour in observable terms: format, length, and what must always be included.
- Wrong
- The ways it can fail, ranked, with one named as unacceptable.
- From
- The sources it may use, and how each claim shows where it came from.
- Test
- The real and awkward cases you will check it against, and who judges them.
- Bar
- The score that ships, written down before the first result comes back.
What you will be able to do
One move, learned well enough to be assessed on it
Product managers, product owners and anyone who writes requirements for software that contains a model.
- 01Write a Definition of Good: six lines an engineer can test against on day one
- 02Rank the ways a feature can fail, and name the one you will not ship
- 03Set a measurable bar before the first result comes back
How it works
Learn it, apply it, have a person check it
Step 1
Learn the model
Short lessons built around one worked example. No video to sit through.
Step 2
Make a call
Judge a realistic situation and see why each option is right or wrong.
Step 3
Make it about your work
Tell us your role and what fills your week. Your final exercise and next steps adapt to it.
Step 4
Submit for review
Write a short applied answer. A TAI Labs reviewer reads it and replies by email.
The credential
AI-Native Product Management: Foundations
Issued only when a TAI Labs reviewer has read your applied exercise and every criterion is met. Anyone can verify it from its credential ID. It is an introductory credential, not a full certificate: that is AI Native Product Management Certificate.
- Defining expected AI behaviour
- Ranking failure modes
- Setting a measurable acceptance bar
How the reviewer decides
- Specific behaviour
- A stranger could tell whether a given output meets In and Out.
- Ranked failures
- At least three failures, ranked, with one named as unacceptable and the harm that would stop you shipping.
- Grounding
- States which sources are allowed and how a reader traces a claim back.
- Test and bar
- Real cases including hard ones, a named judge, and a bar set in advance that covers the unacceptable failure.
- Your own case
- Written for a feature other than the lesson's call summariser, with details the lesson did not give.
Before you start
Questions
Course by Manu Jayawardana, Co-founder.
The free first step of AI Native Product Management Certificate.
- Is it really five minutes?
- The lessons take about five minutes at a steady pace. The final exercise asks you to write, so give it a few extra minutes if you want a strong result. The timer is a guide, never a deadline.
- Do I need an account to start?
- No. We ask where to save your progress after the first lesson, and you only create an account, with a one-time code sent to your email, when you submit the final exercise.
- What is the credential, exactly?
- AI-Native Product Management: Foundations. A TAI Labs reviewer reads your applied exercise against the published rubric, and the credential is issued only if every criterion is met. Anyone can verify it from its ID. It is an introductory credential, not the full AI Native Product Management Certificate.
- Will I be sold to?
- Nothing is sold during the course and we never ask for payment details. At the end we recommend what to learn next, including paid options, and you are free to ignore all of it.
- What happens to my answers?
- They are stored with your account and read by a TAI Labs reviewer. They are never sent to advertising platforms. Your email is not added to marketing emails unless you tick the box that asks for them.
Sources
The framing and examples are TAI Labs originals with fictional companies. These are the published sources the lesson builds on.
- Define success criteria and build evaluations, Anthropic. Success criteria should be specific, measurable and multidimensional, and tests should include edge cases.
- Reduce hallucinations, Anthropic. Restricting a model to supplied documents and requiring quotes is how the From line is made checkable.
- Errors + Graceful Failure, Google PAIR, People + AI Guidebook. Failure modes need to be designed for in advance rather than discovered in use.
- User Needs + Defining Success, Google PAIR, People + AI Guidebook. Agree how success is measured with the people who will live with the result.