Evals are the new unit tests: build the harness before you trust the model
Evaluation sets, graders, regression and what "better" means for an AI workflow
Measuring AI: Evals, Benchmarks & Production Truth
How do you know an AI system is good — on your work, in production, and for the people using it?
The discipline that retires "trust me": build the eval set before you trust the model, read benchmarks as claims to reproduce on your own tasks, monitor what production actually does, and measure the human–AI team rather than the tool.
Lessons in this module
Benchmarks lie gently, so measure on your own work
Saturation, contamination, the jagged frontier and the task-horizon curve
Production is the only benchmark that pays: monitor, drift, feedback
Input and output drift, per-group outcomes, heartbeats and the retrain trigger
Human–AI teaming is a measurable design, not a feeling
AI as tool, teammate or gate; who gains, who deskills, and how to pre-register the metric