Evaluating AI Systems
Learn why evaluating AI is hard and how to build evals that actually predict real-world quality.
intermediate · 45 minutes
Building an AI feature is easy; knowing whether it *works* is the hard part. This course teaches practical evaluation of generative and predictive AI systems: why open-ended outputs resist simple scoring, why public benchmark scores mislead, how to build trustworthy eval sets, and how to combine metrics, human review, and LLM-as-a-judge without fooling yourself. You will leave able to design eval-driven development that catches regressions before your users do.
What you'll learn
- Why Evaluating AI Is Uniquely Hard
- Benchmarks vs. Task-Specific Evals
- Building a Good Eval Set
- Classic Metrics and Their Limits
- Human Evaluation and LLM-as-a-Judge
- Offline, Online, and Eval-Driven Development