AI Product Management
Scope, ship and measure AI features that users trust.
Taught by Kritika Lama · Product Manager
What you'll learn
- Decide whether a problem is a good fit for AI and frame it as a measurable user outcome
- Choose between prompting, retrieval-augmented generation, fine-tuning and agents with clear trade-offs
- Write an AI PRD that defines quality through an evaluation set and acceptance thresholds
- Design user experiences that communicate confidence, allow correction and capture feedback
- Model per-task cost, latency and margin and choose a model tier that fits the business case
- Plan a staged launch with guardrails, monitoring dashboards and an incident playbook
About this course
Product managers are being asked to ship AI features, and the old playbook does not quite fit: outputs are probabilistic, quality is hard to specify, costs scale with usage and users need to know when to trust the result. This intermediate course is for product managers, founders and tech leads who own an AI feature or roadmap and want to make good decisions without becoming machine-learning engineers. You will frame problems where AI genuinely helps, choose between prompting, retrieval, fine-tuning and agents, write an AI product requirements document built around evaluation, design interfaces that handle uncertainty gracefully, model cost and latency into unit economics, and plan a staged launch with monitoring and governance. Every lesson produces a document or artefact you can take into your next planning meeting. You should already know core product management practice; no coding is required, though one lesson includes a small cost-model script.
Course content
6 lessons- 1Framing Problems Worth Solving With AISeparate real AI opportunities from feature theatre and frame them as measurable outcomes.Preview
- 2Choosing the Approach: Prompting, RAG, Fine-Tuning and AgentsUnderstand the main ways to build with models and pick the simplest one that meets the need.
- 3The AI PRD: Requirements Built Around EvaluationWrite requirements that define quality with an evaluation set, thresholds and failure examples.
- 4Designing UX for UncertaintyInterface patterns that show confidence, allow correction and turn user feedback into better models.
- 5Cost, Latency and Unit EconomicsModel per-task cost and margin, choose a model tier and set budgets the business can live with.
- 6Launch, Monitoring and GovernanceShip in stages with guardrails, watch the right metrics and handle incidents and regulation calmly.