Begin with the job, not the model
The capabilities of a model can make a new product possible, but they do not explain why a particular user will choose it. Start with a job that has a clear beginning and end. What information arrives, what decision must be made and who is accountable for the result?
This framing makes tradeoffs easier to see. A slower answer may be acceptable if its sources are reviewable. A fluent answer may be unusable if an error is expensive to detect. Quality is not one universal score; it is a requirement of the task.
Treat verification as part of the product
For many professional tasks, the user still owns the consequences. A product should make it possible to inspect evidence, identify uncertainty and correct an answer. If checking the output takes longer than doing the work, generation speed is a misleading measure of value.
A useful early evaluation set includes ordinary cases, difficult cases and cases where the system should decline to answer. Track how often a person accepts, edits or discards the result, and why. Those distinctions are more informative than counting generated outputs.
Understand what improves with use
A product may become more valuable as it learns a team’s vocabulary, preferences or workflow. But ‘we collect data’ is not a defensibility argument by itself. Ask whether the information is available with appropriate permission, whether it improves an important result and whether the benefit can be measured.
The same discipline applies to integrations. Being connected to many tools is less meaningful than owning a small, important part of the work. A product that consistently resolves one difficult handoff may be harder to replace than a broader interface that people visit only occasionally.
Test the reason for a second use
After a first trial, look for the next naturally occurring task. Does the user return without a reminder? Do they bring more consequential work? Can they explain what they would miss if the product disappeared? These questions reveal whether the product is becoming useful in context.
At the earliest stage, a durable advantage is a hypothesis. It can come from execution, distribution, workflow understanding or a compounding source of quality. The practical task is to discover which mechanism is beginning to work and design a test that could distinguish it from temporary novelty.
Measure the complete job: the output, the checking and the next time someone chooses to use it.