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:
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AI/ML Fundamentals — supervised vs. unsupervised learning, LLMs, RAG, embeddings, hallucination, and the metrics (precision, recall, F1) that actually matter for evaluating a model
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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
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Data & Training Pipelines — data collection, labeling, quality, privacy, and working with vendors and LLM APIs
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Cross-Functional Collaboration — partnering effectively with data science and engineering, agile practices for AI development, and QA/testing approaches unique to AI
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Evaluation, Metrics & Responsible AI — defining success metrics, fairness and bias evaluation, explainability, safety, and compliance
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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.
