Artificial intelligence is reshaping what it means to be a product manager — but most PMs are expected to navigate AI features without ever getting formal training in the concepts, tradeoffs, and risks involved. This course closes that gap.

Through 600 scenario-based practice questions across six comprehensive tests, you'll build a working understanding of what every AI product manager needs to know — not from a data science textbook, but from a PM's actual day-to-day decision-making perspective.

You'll cover:

AI/ML Fundamentals — supervised vs. unsupervised learning, LLMs, RAG, embeddings, hallucination, and the metrics (precision, recall, F1) that actually matter for evaluating a model
AI Product Strategy — when AI is (and isn't) the right solution, build vs. buy vs. partner decisions, and how to prioritize AI use cases
Data & Training Pipelines — data collection, labeling, quality, privacy, and working with vendors and LLM APIs
Cross-Functional Collaboration — partnering effectively with data science and engineering, agile practices for AI development, and QA/testing approaches unique to AI
Evaluation, Metrics & Responsible AI — defining success metrics, fairness and bias evaluation, explainability, safety, and compliance
Go-to-Market & Launch — positioning, pricing, sales enablement, stakeholder communication, and crisis management for AI features

Every question comes with a full explanation, so you understand the reasoning — not just the right answer. Whether you're currently managing AI features, preparing for a PM interview, or simply want to speak confidently about AI in your next roadmap review, this course gives you the practical foundation to do it well.