Most AI Projects fail not due to weak models but due to incorrect problem framing. This course will teach you how to address this key issue.
About the Course
AI Problem Framing for AI teams is what System Design is for engineers and Product Sense is for product managers. Whether you are creating AI solutions, evaluating them, or managing them, this is a fundamental thinking skill that distinguishes real results from endless reworks.
In this course, you will receive:
- The Loop — a 5-step framework (Outcome, Deconstruction, Alternatives, Trade-offs, Signals) for systematically analyzing any AI project
- Live sessions and office hours to discuss your real cases
- Access to over 200 cases — the largest collection of examples of rethinking AI tasks
- Ready-to-use checklists for production (RAG, forecasting, GenAI)
Who This Course is For
This course is for you if:
- Your AI works in demo but "breaks" in production
- You've spent months on a model only to realize you were solving the wrong problem
- You lead an AI direction but come from engineering, product, or management background
What You Will Learn
In 4 weeks, you will review over 200 real AI failures and gain experience that usually takes years. You will master:
- Thinking about AI tasks end-to-end: Defining task boundaries, debugging solutions, and understanding when to change the approach
- Formulating the problem correctly from the start: Using the framework The Loop and asking questions that reveal the core of the problem
- Diagnosing real problems: Understanding what exactly is "broken": the data, the architecture, or the problem framing itself
- Taking responsibility for the task, not just the solution: Transitioning from the role of executor to AI architect and reasonably challenging requirements
- Preventing costly mistakes in the team: Recognizing risks in advance, managing expectations correctly, and translating technical solutions into business language
Intended Audience
- Engineers who can build AI but want to understand what exactly should be built
- Specialists working with RAG, agents, or ML models aiming to bring solutions to production
- Leaders who need to evaluate AI initiatives, plan stages, and make informed decisions
Requirements
- Basic understanding of AI/ML (in terms of concepts and terminology)
- Experience participating in AI projects
- No programming in the course — only thinking and decision-making frameworks
