Eval systems create a flywheel for iteration

The root cause of failed AI products is almost always a failure to build robust evaluation (see: tracing and evaluation serve different jobs) systems. Teams focus on changing behavior (prompts, fine-tuning, new models) without the evaluation infrastructure to know whether the change helped. Without evals, each change is a guess; with them, each change is measured.

The eval flywheel has three levels: unit tests (fast assertions run on every change), human and model evaluation (label traces as good or bad, align an LLM-as-judge with human judgment), and A/B testing (validate with real users). The same infrastructure serves debugging (searchable traces, assertions that flag errors) and fine-tuning (curated data from labeled traces). The investment pays for itself many times over.

Designing the product so its outputs are checkable up front is what gives the flywheel something to measure (see: a hard-to-eval product is hard for users to verify).