This module covers essential AI model concepts, including LLMs, foundation models, model capabilities, and the changing AI landscape, while exploring how Product Managers can select the right AI model and adapt as models evolve.

This module covers the importance of context in AI product work, exploring AI product workspaces, Product OS, context files, and the limitations of generic AI outputs while providing hands-on practice in building a strong product context system.

This module covers how AI Product Managers connect tools, design information, MCPs, codebases, and live product data, while exploring safe tool connections and ways to refine, publish, and reuse AI-generated product artifacts.

This module covers reusable AI commands and workflows, exploring stakeholder feedback simulation, continuous workflow improvement, and how product context becomes more valuable over time through learning and iteration.

This module covers AI product orchestration, exploring user signals, AI product experiences, functional evaluations, feedback loops, and continuous improvement while bringing these elements together into an end-to-end AI product-building workflow.

This module covers AI product governance, including evaluations, guardrails, monitoring, risk management, trust, cost, and defensibility, while examining real-world challenges and how Product Managers can build safer and more reliable AI products.

This module covers the human skills essential to AI Product Management, including vision, empathy, taste, communication, and judgment, while exploring outcome ownership, skill development, portfolio building, and preparation for AI Product Manager careers.

This module covers practical collaboration between Product, Design, and Engineering teams, exploring AI product planning, user context, trade-offs, prototyping, engineering handoffs, design critique, and iterative improvement of AI product experiences.

This module covers the final stage of the AI Product Manager journey, including a proficiency quiz, bonus content, course completion, and reflection on how learners can continue developing and applying their AI Product Management skills.