Starweaver

Segment 00: Welcome to the Course: Course Overview

Segment 01: Introduction to the Course

Segment 02: Module Introduction

Segment 03: How AI Products Differ from Traditional Software

Segment 04: AI Capability Types and Product Use Cases

Segment 05: AI Product Maturity: Rule-Based to AI-Native

Segment 06: Framing AI Features as User Problems, Not Technology Demos

Segment 07: Data Readiness for PMs: Availability, Quality, and Labeling

Segment 08: Common Pitfalls in AI Product Discovery

Segment 09: Writing AI Feature Requirements and Defining Performance Boundaries

Segment 10: Choosing Between Prompt-Only, RAG, Fine-Tuning, and Agents

Segment 11: Minimum Viable Model Thinking for AI MVPs

Segment 12: Reading: AI Readiness and Strategy for Product Leaders

Segment 13: Hands-On-Learning: Evaluate and Scope an AI Feature Opportunity

Segment 14: Quiz: Scoping AI Features and Choosing the Right Solution

Segment 15: Module Introduction

Segment 16: Precision, Recall, Accuracy, and F1 for Product Managers

Segment 17: Defining AI Success Metrics That Bridge Model and Product Outcomes

Segment 18: When Is the Model Good Enough to Ship?

Segment 19: UX for Non-Deterministic and Probabilistic AI Outputs

Segment 20: Designing Transparency, Confidence, and Human Oversight

Segment 21: Feedback Loops That Improve AI Products Over Time

Segment 22: Bias, Privacy, and Regulatory Risk in AI Products

Segment 23: Launch Strategies, Monitoring, and Post-Launch AI Operations

Segment 24: Roadmapping AI Products: Model Iteration, Features, and Cost Trade-Offs

Segment 25: Course Wrap-Up

Segment 26: Reading: Designing AI Interfaces for Trust and Transparency

Segment 27: Hands-On-Learning: Build an AI Launch-Readiness Scorecard

Segment 28: Quiz: Launching, Governing, and Scaling AI Products

Segment 29: Project: Create an AI Product Brief for a Trustworthy AI Feature