Full course coming soon

Problem Framing and Verification for AI-Assisted Workflows

7 chapters · 31 lessons

1. 1. Problem Decomposition for AI Systems

   4 lessons

   Learn to break down ambiguous requests into well-defined tasks that AI can handle reliably.

2. 2. Context Engineering for Better Outputs

   4 lessons

   Provide the right background, examples, and constraints to improve AI output quality.

3. 3. Verification Strategies for Generated Code

   5 lessons

   Build systematic checks to catch errors, security issues, and maintainability problems in AI-generated code.

4. 4. Validating Requirements and Specifications

   4 lessons

   Assess AI-generated product requirements, tickets, and specs for completeness and feasibility.

5. 5. Designing Human-in-the-Loop Workflows

   5 lessons

   Decide where AI fits in your process and where human review is required.

6. 6. Evaluating AI System Limitations

   4 lessons

   Understand when AI outputs are unreliable and how to recognize failure modes.

7. 7. Building Accountability into AI-Assisted Work

   5 lessons

   Maintain professional responsibility and quality standards when using AI tools.