Product Management line, station 10 of 11AI Engineering line, station 6 of 6
AI Product Management
Decide where AI belongs, design AI features people can trust, measure their quality with evals, and roll out operations agents that earn their autonomy and pay for themselves.
- 5lessons
- 3 hto read and do
- Advancedlevel
- 5podcast episodes
- 1checked exercises
- 1simulators
Tools you'll use in this course
The lessons open these with a template ready. They work for your own job too, no course needed.
About this course
AI features fail in ways ordinary features don't. They are right most of the time, wrong some of the time, and the wrong answers often look exactly like the right ones. Managing them well takes a few things most PMs were never taught: enough understanding of models, retrieval and tools to discuss cost, speed and failure with engineers; the judgement to pick problems where AI beats a rule; and a way to measure quality that leadership can trust.
This course teaches those methods step by step. You will estimate the cost per answer and set a response-time budget, decide between running a model on your own server and calling an API on privacy, cost, speed and quality, and screen AI ideas against their boring alternatives. You will design answers that show their sources and say "I don't know", write failure states and privacy and safety guardrails as code, build a golden set, check an LLM judge against human labels, and link eval scores to product metrics. The last lesson takes an assistant from answering questions to drafting fixes for approval to running routine tasks within limits, with audit trails, staged rollout and an honest ROI.
Every lesson follows Imran, the product manager for Mangrove Market's Store app, as his team builds Ask Ops: an in-house assistant that answers store managers' 3,000 weekly questions from procedures and live data, and later drafts supplier corrections for a manager to approve. The course stays vendor-neutral and links to the engineering lessons for the technical depth.
What you will be able to do
- Explain how an AI feature works (model, context, retrieval, tools, fine-tuning) well enough to estimate cost per answer and response time, and to discuss failure modes with engineers
- Compare running a model on your own server with calling an API on privacy, cost per query, response time and quality, using break-even volume and a test set
- Pick problems where AI beats rules, price the cost of being wrong per type of case, and say no with a better alternative
- Design AI features with the right human-in-the-loop steps, trust signals, failure states, and privacy and safety guardrails enforced in code
- Build a golden set, run error analysis, check an LLM judge against human labels, and link eval scores to product metrics
- Scope an operations agent's autonomy per task type, its tools, permissions, approvals and audit trail, and plan a staged rollout with a measured ROI
Syllabus
5 stops, about 3 h in total. Each stop fills in when you complete the lesson.
How AI features work, just enough for PMs
Welcome to AI Product Management. You don't need to build models to manage AI products, but you do need to know what happens between a question and an answer, because that is where the cost, the waiting time and the mistakes come from. This lesson gives you that picture, the arithmetic behind it, and a fair way to decide between running a model on your own server and calling an API.
AI fundamentals for PMsCost and latency budgetsHosting decisions
Picking the right problems for AI
Every leadership team now has a list of things to "do with AI". Some are excellent fits. Many are better solved with a rule, a report or a better form, and a few are dangerous. This lesson gives you a quick, honest way to tell them apart, and the words to say no without sounding afraid of the future.
AI opportunity screeningCost of errorsAssist vs automate
Designing AI features people can trust
An AI feature will be wrong some of the time, so the design decides what happens next: whether people notice, whether they can recover, and whether anyone learns from it. This lesson covers the parts of AI design that ordinary features don't need: showing uncertainty, keeping the right person in the loop, designing failure states, privacy and safety guardrails, and feedback you can actually use.
AI UX and guardrailsHuman in the loopPrivacy and safety guardrailsFeedback capture
Evals: measuring AI quality
Without evals, "the new prompt seems better" is a feeling. With them, it is a number you can check per category, before and after every change. This lesson shows the method strong AI teams use: read real outputs and count the failures, build a golden set, grade with code, a model judge and people, check the judge against human labels, and link the scores to the product metrics leadership cares about.
Evals and error analysisGolden setsLLM-as-judgeLinking evals to product metrics
AI agents in operations
An assistant that answers questions saves people time. An agent that does the work saves more, and can do more damage. This lesson shows how to move an operations assistant from chat to suggested tasks to automated tasks with approvals: how to set its autonomy per task, which tools and permissions to give it, how approvals and audit trails work, how to roll it out in stages, and how to measure whether it pays for itself.
Agent autonomy and permissionsApprovals and audit trailsStaged rolloutAgent ROI
Capstone: Ask Ops: an AI assistant for 320 store managers
A realistic project with a rubric and a model answer.
Final exam and certificate
10 questions drawn at random, 70% to pass.
Capstone project
Ask Ops: an AI assistant for 320 store managers
Mangrove Market's store managers send head office about 3,000 questions a week, such as "why is this GRN stuck?" and "why was my order cut?". Eight people on the ops desk spend about 300 hours a week answering them, and answers take about three hours. Propose an AI assistant that answers from Mangrove's procedures and systems, and later drafts fixes, such as supplier corrections, for a manager to approve. Compare it honestly with two other AI candidates, specify how it behaves and what it may do, build the golden set you would grade it with, estimate what it costs, and plan how it reaches all 320 stores without a privacy incident or a wrong refund along the way. Use the fictional procedures from the course (RF-2 refunds, RC-1 receiving, EX-3 expiry, ST-4 stock counts, PR-1 prices, CD-1 customer data and others), or write your own.
You will hand in
- A one-page memo comparing the assistant with two other AI candidates, each with its boring alternative, cost of being wrong and starting level
- A design spec with the autonomy level per task type, approval steps, answer card, failure states, and privacy and safety guardrails
- A 50-case golden set with a pass/fail rubric, and an error-analysis table from a test run of at least 30 answers
- An estimate of tokens and cost per answer and per month across 320 stores, at normal volume and festival peak, with a hosting recommendation
- A staged rollout plan with gates, the metrics that would make you pause it, and an ROI model for the first agent task type
Check what you know
Already know some of this? Answer 5 quick questions, one from each lesson, and we will mark the lessons you can skim. It takes about 3 minutes and nothing is graded.
Questions
Do I need to code or train models for this course?
No. You need to read a simple formula and a short JSON example. One optional practice task in the evals lesson is a small JavaScript exercise that runs in your browser. The course is about decisions, design and measurement, and it links to the engineering lessons for the technical depth.
Which AI tools or models do I need?
None in particular. The course is vendor-neutral and names models and tools only as examples, because they change every few months. For the capstone's test run you can use any AI assistant your company allows, with fake or public data, or label sample answers you write yourself.
How is this different from AI Systems for Builders and Building AI Agents?
AI Systems for Builders teaches engineers how retrieval, vector search and model serving work, and this course links to it instead of re-teaching it. The coming Building AI Agents course goes inside agent tool loops. This course covers the product side: which problems to pick, how the feature behaves, how quality is measured and how agents are rolled out and paid for.
How long does the course take?
About three hours for the five lessons and their practice tasks, plus the podcast episodes. The capstone takes about a week of part-time work, mostly writing and labelling the golden set and running a small test.
Are the prices in the course real?
No. Model prices change often, so the course counts tokens and asks you to use your provider's current price page. Mangrove's costs per answer and server quotes are fictional numbers chosen to show the method.
Is it really free?
Yes. Every lesson, podcast episode, exercise, quiz, final exam and certificate is free. The labs and their templates for real work are free too. Plus and Premium add every lab challenge, weekly extras and a mentor on top, and they never lock the tools or the courses.
How does the final exam work?
You get 15 questions drawn at random from a larger bank. You need 70% to pass. If you do not pass, you can try again after 24 hours. The exam is graded on the server and you never see the answer key.