Full course coming soon

Building Feedback Loops for AI Systems

7 chapters · 30 lessons

1. 1. The Anatomy of a Learning Loop

   4 lessons

   Map the four stages of AI feedback cycles and understand why loops create compounding advantage over static deployments.

2. 2. Capturing Evaluation Signals

   5 lessons

   Identify which human interactions with AI outputs contain valuable feedback and design instrumentation to capture them.

3. 3. Routing Feedback into System Improvements

   4 lessons

   Match feedback types to improvement mechanisms and build pipelines that automate the flow from signal to update.

4. 4. Organizational Design for Closed Loops

   4 lessons

   Structure teams, incentives, and workflows so feedback actually reaches the people who can act on it.

5. 5. Diagnosing and Fixing Broken Loops

   5 lessons

   Identify common failure modes where feedback is collected but never used, and implement fixes that restore loop integrity.

6. 6. Real-World Loop Patterns by Use Case

   4 lessons

   Apply feedback loop design to customer support, content generation, code assistance, and decision support scenarios.

7. 7. Scaling and Sustaining Loop Operations

   4 lessons

   Build infrastructure and processes that keep loops running as usage grows and the organization evolves.