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AI System Evaluation and Quality Assurance for Business Teams

7 chapters · 31 lessons

1. 1. Defining Success Criteria for AI Systems

   4 lessons

   Learn to translate business goals into measurable AI performance requirements.

2. 2. Building Evaluation Test Sets

   4 lessons

   Create representative datasets that reveal how AI performs on real-world inputs.

3. 3. Interpreting Performance Metrics

   5 lessons

   Understand precision, recall, F1, and other metrics to judge model readiness.

4. 4. Detecting Bias, Hallucination, and Safety Risks

   5 lessons

   Identify failure modes that harm users or violate policy before they reach production.

5. 5. Human in the Loop Quality Assurance

   4 lessons

   Design workflows that combine AI automation with human judgment and oversight.

6. 6. Monitoring AI Systems in Production

   5 lessons

   Track performance drift, user satisfaction, and operational health after launch.

7. 7. Collaborating with Technical Teams on AI Quality

   4 lessons

   Communicate evaluation findings and partner with engineers to iterate on models.