AI & product
Start with the task, not the AI model
An AI feature earns its place when it helps someone finish a real task. Before choosing a model or designing a chat box, write down what the person is trying to accomplish, what gets in their way, and what a good result looks like.
Make the first use case small
Consider a study app. ‘An AI tutor’ leaves many decisions open. ‘Help a student understand one incorrect answer, using the lesson they are studying’ gives the team a clear interaction to design and evaluate. The input, context, and expected result become much easier to discuss.
Write a short brief: who needs help, when they need it, what information is available, and what they should be able to do afterwards. Keep the first release focused enough that you can review its outputs carefully.
Design the correction path
Treat the first answer as something the user may need to question or revise. A useful interface can let them edit the input, ask for a different explanation, inspect the material behind an answer, or return to the original task without losing their work.
For an AI writing assistant, this might mean showing a suggested draft beside the original and asking the user to accept changes. For a learning tool, it might mean revealing a hint before a full explanation. Choose the amount of automation around the task and the consequences of a mistake.
Evaluate the whole experience
Create a small set of realistic examples before the first demo. Include incomplete questions, ambiguous wording, and requests outside the intended scope. Review whether the feature gives a useful result and whether the interface helps someone recognise a poor one.
Choose measures that reflect the job: can a student explain the next step, can a person finish editing their draft, or can a team review a document with less repetition? Record failures alongside successful examples. A fluent answer alone does not establish that the task was completed.
Give the feature a clear boundary
Tell people what the feature can help with and what they need to check. Decide what happens when information is missing or the request cannot be handled. A clear request for more context is often more useful than an answer that looks complete but rests on an assumption.
The first milestone should be a complete, understandable workflow for one task. Once people can use it, correct it, and get a useful result, there is a stronger basis for deciding what to add next.