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Designing Feedback Loops for AI Systems

7 chapters · 31 lessons

1. 1. Defining Success Metrics for AI Systems

   4 lessons

   Translate business goals into measurable objectives that guide AI system development and evaluation.

2. 2. Building Evaluation Datasets and Test Suites

   4 lessons

   Create representative test sets that reveal real-world performance and catch regressions early.

3. 3. Designing Controlled Experiments for AI Changes

   5 lessons

   Structure experiments that isolate variables and produce reliable evidence about system improvements.

4. 4. Architecting Systems with Models, Tools, and Retrieval

   5 lessons

   Combine language models, vector search, external tools, and structured data into cohesive workflows.

5. 5. Implementing Continuous Feedback Loops

   4 lessons

   Capture production signals and route them back into evaluation, training data, and system refinement.

6. 6. Preventing Overfitting to Benchmarks and Eval Sets

   4 lessons

   Recognize and avoid common failure modes where systems optimize for tests instead of real performance.

7. 7. Case Study: Building an Improvement Pipeline End to End

   5 lessons

   Walk through a complete example of designing, running, and iterating on an AI system with structured feedback.