AI Product Management
Product managers, analysts and project leads who want to run AI products, and career changers aiming for an AI product role without becoming an engineer
AI Product Management is an online course for people moving into AI product roles. It covers how AI products fail, feasibility and cost, writing requirements with thresholds and fallbacks, evaluation, and launch. You build three portfolio documents and receive a certificate on completion.
The job of deciding what an AI product should do when the product is right most of the time and never all of the time. You learn how these systems fail, how to judge feasibility and cost before a build starts, how to write requirements that specify thresholds and fallbacks, how to set a release bar with an evaluation set, and how to make the case to leadership. You finish with three portfolio documents: a requirements document, an evaluation suite run against a real model, and a teardown with a build-or-buy recommendation.
Coming soon
What you'll be able to do
- Explain why an AI product fails differently from ordinary software, and design the product around those failures
- Choose between prompting, retrieval, fine-tuning and buying a vendor, and defend the choice on cost and risk
- Estimate what an AI feature costs per active user before anyone builds it
- Write a requirements document with confidence thresholds, failure modes, human review and fallback behaviour
- Set a release bar with an evaluation set, and know when a model-judge can be trusted
- Present an AI launch to leadership with its risks, its costs and a clear recommendation
- Run a job search for AI product roles with three portfolio documents to show
Modules in this course
Why managing a product that is right most of the time is a different job from managing ordinary software, and what the role looks like in a real week.
9 min read
Tokens, context, sampling and retrieval explained without maths, and the four causes behind almost every wrong answer a user reports.
9 min read
A screening method for proposed AI features: where the technology is strong, where errors are survivable, and when a simpler solution wins.
8 min read
The four ways to deliver an AI feature, what each is good for, what it commits the organisation to, and how to choose without being sold to.
10 min read
How to estimate what an AI feature costs to run before it is built, where waiting time comes from, and why the cheapest model that passes is the right one.
9 min read
The sections an AI requirements document needs that an ordinary one does not: quality bar, confidence thresholds, failure modes, fallbacks and guardrail metrics.
10 min read
Interface patterns that absorb AI errors, how to design review that people actually do, and how to earn trust without overpromising.
9 min read
What data an AI feature needs, how to specify it, and the working habits that make a product manager useful to engineers rather than an obstacle.
9 min read
How to build the evaluation set that defines 'good enough', when a model can grade outputs and when it cannot, and how to turn results into a launch decision.
11 min read
What to measure once real users arrive, why quality can change without any release, and how to decide between fixing forward and rolling back.
9 min read
The obligations an AI product manager must recognise, including the EU AI Act risk tiers and data protection law, and how to run a risk review before launch.
10 min read
How to present an AI investment honestly, with costs, risks and a clear recommendation, and how to decline an AI project leadership wants.
9 min read
How to get an AI product role from where you are now: which routes work, what to show, and how AI product interviews test judgement.
10 min read
What to learn, measure and change first when you join an AI product team, and the early mistakes that cost credibility.
8 min read
A complete AI requirements document for a feature in a real product, with a quality bar, a threshold table, failure modes, fallbacks and guardrail metrics.
300 min to build
A golden set of at least 25 real cases, run three times each against a real model, with a validated model judge and a written launch recommendation.
300 min to build
A teardown of a shipped AI product's failure handling, and a costed build-or-buy memo recommending how an organisation should get the same capability.
240 min to build
Questions before you buy
- How long does this course take?
- About 2 hours of reading, plus roughly 14 hours across the 3 build projects. Most of that time is the build projects, at your own pace.
- Do I need any experience first?
- No formal prerequisites — every course is written to be readable from the first lesson, and that lesson is free to read before you buy, so you can check it's pitched right for you.
- How is the certificate issued?
- Automatically, the moment you complete every module and project. No review queue to wait on.
- What's your refund policy?
- A 14-day money-back guarantee, no questions asked — email support@courseparse.com within 14 days of purchase. Full policy on the Refunds page.