An impressive demonstration is a beginning. A useful product has to work repeatedly, under ordinary conditions, for people who are busy doing something else. That distance between a demo and daily use is where the most interesting engineering happens.
Begin with the task
Start by describing the work a person is trying to complete. What information do they need? Where do they lose time? Which errors matter most? Those questions make it easier to judge whether an AI system is helping.
A model’s headline score cannot answer every product question. The relevant evaluation should reflect the task, the data it will encounter, and the consequences of getting something wrong.
A useful tool earns its place in the ordinary day.
Design for uncertainty
A system should make uncertainty visible rather than bury it behind a confident interface. A reader needs to know when a result is a suggestion, when evidence is missing, and when a human decision is required.
That also means providing a route back to the original information. A useful summary should help someone inspect the underlying material, not replace their ability to check it.
Make the ordinary experience better
Reliability, latency, cost, and maintenance determine whether a tool is practical. A smaller improvement that consistently saves a few minutes can be more valuable than a dramatic feature that seldom works.
The goal is a product people can understand and trust within its limits. Good engineering makes those limits clear and spends effort on the parts of the experience that repeat every day.
