Ask whether the task tolerates being wrong. If a mistake is cheap to spot and cheap to undo, a probabilistic feature works well; if the error is silent or expensive, better rules and better defaults win. Then check the data exists and that users are not already avoiding the workflow, because adding a model to a workflow nobody opens just makes an unused workflow slower.
Why interviewers ask this
In 2026 every roadmap has pressure to add a model, so interviewers want to know if you can resist it intelligently. They are listening for a cost of error framing, for awareness of data availability as a gating constraint, and for the discipline to name a case where the simpler answer was correct. Enthusiasm without a failure analysis reads as someone who will burn a quarter on a demo.
How to structure your answer
- Lead with the cost of a wrong output and how visible it is.
- Check the workflow is actually used before making it smarter.
- Confirm the required data and context are available.
- Scope the smallest version that tests whether users trust the output.
Example answer
The question I ask first is what happens when it is wrong, because that determines everything. If the user sees the mistake immediately and can undo it cheaply, a model is a great fit, which is why drafting and summarizing work so well. If the error is silent or expensive, like something that changes a number a customer bills on, I would rather have rules and a much better default. The second question is whether the underlying task is even wanted. I have seen a lot of effort go into adding intelligence to a workflow people were already avoiding, which just produces a smarter version of something nobody opens. And third, the data has to exist. If the model needs context living in three systems we have no access to, the honest answer is that this is an integrations project first. Then I scope the smallest version that tests whether people trust the output at all.
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See how it worksFollow-up questions to expect
- How would you set expectations in the UI for an output that is sometimes wrong?
- What does a good evaluation setup look like before you ship?
- How do you handle the cost per request in the pricing model?
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