This is a practical, workflow-first course for product managers who want to use AI as a daily co-pilot – not as a novelty tool, but as a reliable system that reduces the administrative load of PM work at every stage of the product lifecycle.
Across 9 modules and more than 30 lessons, you will cover every phase of product development – from discovery and requirements through development, testing, deployment, and maintenance – and learn exactly how to apply agentic AI at each stage. Nearly every lesson includes ready-to-use prompt templates you can copy, adapt, and run immediately in ChatGPT or Claude.
What is inside each lesson:
Every lesson in this course is built around a practical learning stack:
- Audio podcast lesson: Narrated lesson you can consume on the go – commuting, walking, or between meetings
- Infographic: Visual summary of the key frameworks, tables, and concepts from the lesson
- Mind map: Structured visual overview of how the lesson fits into the broader module and course
- Lesson notes database: Annotated reference notes for each lesson – searchable, scannable, ready to revisit
- Slide deck: Presentation-ready slides for each lesson – useful for sharing frameworks with your team
- Quiz (Q&A document): A comprehensive question bank covering all modules so you can test and reinforce your learning
Who this course is for:
- Product managers who spend too much time writing, organizing, and documenting instead of thinking
- PMs new to AI tools who want a structured, practical starting point
- Experienced PMs who have experimented with ChatGPT but want a systematic workflow approach
- Scrum Masters, product owners, and project leads who run ceremonies and manage stakeholder communications
No engineering background required. All prompts are written in plain language and explained with context.
What you will be able to do after this course:
- Run an AI-assisted discovery session and convert raw interview notes into structured requirements in under 30 minutes
- Generate a full backlog with acceptance criteria, story splits, and a definition of done from a feature brief
- Use AI to plan sprints, simulate scope trade-offs, and manage capacity without spreadsheets
- Generate multi-audience release notes from a list of closed tickets – in one prompt
- Reconstruct an incident timeline, identify root cause, and draft a stakeholder communication package within an hour of resolution
- Facilitate retrospectives that produce tracked, completed action items instead of forgotten sticky notes
- Build and maintain a cross-sprint pattern library so recurring problems become visible before they become normalized
