Building AI-Powered Products: A Guide for Product Managers

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Building AI-Powered Products: A Guide for Product Managers

Instructors: Starweaver

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2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Differentiate AI product opportunities from traditional software features and identify where AI creates measurable user and business value.
Evaluate data readiness, feasibility constraints, and solution patterns to scope viable AI features for real product use cases.
Define practical success metrics, trust signals, and evaluation criteria for launching AI features with confidence.
Apply human oversight, governance-aware practices, and risk controls to design trustworthy AI product experiences.

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There are 2 modules in this course

Learn AI-Powered Products for Product Managers and master how to scope, build, and launch successful AI features with confidence. This intermediate course teaches you to identify AI-worthy opportunities, assess data readiness, and choose the right solution pattern—prompt-only, RAG, fine-tuning, or agents. Ideal for experienced PMs, technical PMs moving into AI/ML, and founders building AI-first products, it replaces AI hype with practical frameworks, hands-on labs, role-play activities, and a capstone project resulting in a complete AI Product Brief.

Gain in-demand skills in AI feature scoping, metrics interpretation, trustworthy AI UX, and governance readiness. You'll learn to translate precision, recall, and F1 scores into product decisions, define launch-readiness criteria, and design for trust through transparency and human oversight—while covering compliance essentials like the EU AI Act and NIST AI RMF. Taught by Karlis Zars, this course delivers reusable PRD templates, opportunity assessment tools, and launch scorecards to help you ship AI features that create real business and user value.

This module helps learners shift from traditional product thinking to AI product thinking. It introduces how AI-powered products differ from conventional software, how to identify strong AI opportunities, and how to validate whether a problem is actually worth solving with AI. Learners then move into scoping AI features, assessing data readiness, defining inputs and outputs, and choosing the right solution pattern such as prompt-only, retrieval-augmented generation, fine-tuning, or agents.

What's included

11 videos3 readings1 assignment1 peer review2 discussion prompts

11 videosTotal 79 minutes

Welcome and Course Goals 4 minutes
Module Introduction 3 minutes
How AI Products Differ from Traditional Software 7 minutes
AI Capability Types and Product Use Cases 7 minutes
AI Product Maturity: Rule-Based to AI-Native 7 minutes
Framing AI Features as User Problems, Not Technology Demos 7 minutes
Data Readiness for PMs: Availability, Quality, and Labeling 8 minutes
Common Pitfalls in AI Product Discovery 7 minutes
Writing AI Feature Requirements and Defining Performance Boundaries 8 minutes
Choosing Between Prompt-Only, RAG, Fine-Tuning, and Agents 11 minutes
Minimum Viable Model Thinking for AI MVPs 8 minutes

3 readingsTotal 15 minutes

Welcome to the Course: Course Overview5 minutes
AI Readiness and Strategy for Product Leaders 5 minutes
Fine-Tuning vs RAG vs Agents: A Practical Comparison 5 minutes

1 assignmentTotal 20 minutes

AI Product Thinking, Scoping, and Solution Design20 minutes

1 peer reviewTotal 10 minutes

Hands-On-Learning: Evaluate and Scope an AI Feature Opportunity10 minutes

2 discussion promptsTotal 20 minutes

Pressure-Testing an AI Opportunity in Your Own Work10 minutes
Where AI Requirements Are Hardest to Write in Your Context10 minutes

This module focuses on what happens once an AI feature moves toward launch. Learners explore how to measure AI performance in product terms, interpret ML metrics such as precision, recall, accuracy, and F1, and define practical thresholds for when a model is good enough to ship. The module then shifts into trust and risk, including UX for non-deterministic outputs, transparency design, human-in-the-loop patterns, bias, fairness, privacy, and governance. Finally, learners examine how to launch, monitor, and scale AI products through phased rollouts, shadow testing, post-launch monitoring, cost awareness, and roadmap planning.

What's included

11 videos2 readings1 assignment2 peer reviews2 discussion prompts

11 videosTotal 78 minutes

Module Introduction 3 minutes
Precision, Recall, Accuracy, and F1 for Product Managers 8 minutes
Defining AI Success Metrics That Bridge Model and Product Outcomes 8 minutes
When Is the Model Good Enough to Ship? 7 minutes
UX for Non-Deterministic and Probabilistic AI Outputs 8 minutes
Designing Transparency, Confidence, and Human Oversight 8 minutes
Feedback Loops That Improve AI Products Over Time 8 minutes
Bias, Privacy, and Regulatory Risk in AI Products 9 minutes
Launch Strategies, Monitoring, and Post-Launch AI Operations 9 minutes
Roadmapping AI Products: Model Iteration, Features, and Cost Trade-Offs 8 minutes
Course Wrap-Up3 minutes

2 readingsTotal 10 minutes

Designing AI Interfaces for Trust and Transparency 5 minutes
Artificial Intelligence Risk Management Framework 5 minutes

1 assignmentTotal 20 minutes

Measuring, Designing Trust, and Shipping AI Products Responsibly20 minutes

2 peer reviewsTotal 40 minutes

Hands-On-Learning: Build an AI Launch-Readiness Scorecard10 minutes
Project: Create an AI Product Brief for a Trustworthy AI Feature30 minutes

2 discussion promptsTotal 20 minutes

Spotting Trust Design in an AI Product You Already Use10 minutes
The Hardest Part of a Phased AI Launch in Your Organization10 minutes

Instructors

Starweaver

STARWEAVER

606 Courses1,250,343 learners

Offered by

STARWEAVER

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