This course, titled “AI Problem Framing,” addresses a key reason many AI projects fail: improper problem formulation. It provides a fundamental skill set for AI teams, similar to System Design for engineers and Product Sense for product managers. The course is designed for anyone involved in creating, evaluating, or leading AI solutions.
Course Outline:
- The Loop Framework: A 5-step process (Outcome, Deconstruction, Alternatives, Trade-offs, Signals) for systematic analysis of AI projects.
- Live Sessions and Office Hours: Opportunities to discuss real case studies.
- Case Study Access: Over 200 examples of redefined AI problems.
- Production Checklists: Ready-to-use resources for risk assessment, forecasting, and generative AI.
Target Audience:
- Engineers who can build AI but wish to understand the underlying problems.
- Specialists working with RAG, agents, or ML models who seek to bring solutions to production.
- Managers responsible for evaluating AI initiatives and making informed decisions.
What You Will Learn:
In four weeks, participants will analyze over 200 real AI failures, gaining years of experience in understanding end-to-end AI tasks. You will learn how to:
- Define task boundaries and adjust solutions effectively.
- Use The Loop framework to formulate problems correctly.
- Diagnose root issues in AI projects, such as data or architecture flaws.
- Take accountability for the problem definition and not just solutions.
- Identify risks early, manage expectations, and communicate technical solutions in business terms.
Requirements:
- Basic understanding of AI/ML concepts and terminology.
- Experience in AI projects.
- No programming needed—focus is on frameworks for thinking and decision-making.