AI Needs an Evaluation Strategy
A useful AI system starts with a clear definition of quality, not a model selection exercise.
- AI
- Engineering
An AI prototype can be persuasive after a handful of successful demonstrations. Production systems are judged differently. They must behave usefully across ordinary cases, difficult cases, and the cases nobody anticipated during the demo.
Before choosing a model, define what a good answer means. That definition should include factual quality, acceptable variation, latency, cost, and the conditions under which the system should decline to answer.
Evaluation is not a single benchmark performed before launch. It is an operating capability. Real usage reveals new failure modes, changes the distribution of inputs, and exposes differences between what users say they need and what actually helps them.
The strongest teams maintain representative evaluation sets, inspect failures systematically, and connect product feedback to technical changes. They treat prompts, models, retrieval, and interface design as parts of one system.
AI removes complexity only when the uncertainty inside it is made visible and managed deliberately.