An AI feature is valuable when it helps someone complete a real task—not simply because a model can be connected to an interface. The best starting point is a specific user need, a clear success measure and an honest understanding of what the system may get wrong.
Start with the job, not the model
Identify the repetitive or information-heavy step that slows a user down. Then ask whether AI is the right tool. A fixed rule or a better search may be cheaper, faster and more predictable. AI can be a good fit when inputs vary and language or pattern recognition is central to the task.
Make the feature’s limits visible
AI output can be incomplete, out of date or confidently wrong. Interface design should set expectations, provide useful context and make it straightforward to review, correct or reject a suggestion. For consequential decisions, keep a qualified person responsible for the final judgement.
Evaluate with realistic examples
A few impressive demonstrations do not show how a feature performs across everyday use. Build a representative set of examples, define what a good result means, and test difficult as well as common cases. Track failures that matter to users, such as missed information, unsupported claims or an answer that is hard to act on.
Protect user data and plan for operations
Before launch, decide what information the feature needs, where it is processed, how long it is retained and who can access it. Consider latency, service availability, usage costs and a useful fallback if the AI service is unavailable. These choices affect both user trust and the product’s long-term sustainability.
A dependable AI feature combines a well-chosen task, careful evaluation, thoughtful controls and ongoing monitoring. The model matters, but the surrounding product decisions determine whether people can use it with confidence.
Source: OpenAI Platform — Evaluation best practices
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