Full course coming soon

Testing and Validating AI Systems in Production

7 chapters · 34 lessons

1. 1. From Model Benchmarks to System Performance

   4 lessons

   Understand why generic AI benchmarks fail to predict real-world performance and how to shift evaluation focus to operational outcomes.

2. 2. Designing Role-Specific Evaluation Frameworks

   5 lessons

   Build evaluation frameworks tailored to specific job functions like alert triage, content moderation, or code review.

3. 3. Testing Agent Behavior and Tool Use

   5 lessons

   Evaluate how AI agents plan actions, call tools, retrieve information, and adapt based on intermediate results.

4. 4. Building Regression Test Suites

   5 lessons

   Create automated test suites that catch performance degradation when system components change.

5. 5. Operational Guardrails and Safety Checks

   5 lessons

   Implement runtime guardrails that prevent harmful actions and ensure AI systems respect permissions and policies.

6. 6. Measuring Reliability and Repeatability

   5 lessons

   Test whether AI systems produce consistent, reliable results across time, data variations, and edge cases.

7. 7. Production Monitoring and Continuous Evaluation

   5 lessons

   Deploy monitoring systems that track AI performance in production and trigger re-evaluation when behavior degrades.