Evaluation is the specification of the AI system.
This paragraph is worth reading again and again: evaluation is basically the specification of the AI system.
When building enterprise AI, you can’t just say “the accuracy rate must be high.” Success must be defined as a number that the business can understand: how much deflection rate is acceptable, what level of false positive can be controlled, which questions must be handed over to humans, and which answers are considered failures.
Test cases should also come from real user problems.
Demo data can easily make the system look good, and production data will bring out all the boundaries.
Without evaluation, it would be difficult for the team to judge whether the system is getting better or worse after it goes online.
What's even more troublesome is that every time the prompt, model, and data are updated, there is no reliable way to judge whether the changes have harmed other scenes.