The problem
What was broken before AI
AI product teams can move too quickly from idea to UI. A team may design a beautiful chat screen, search box, or assistant experience before answering the harder questions: what will users actually ask, what context does the model need, when should it ask a follow-up, what should it refuse or escalate, and how will the user know whether to trust the answer? Without sample conversations, the interface can look finished while the product behavior remains vague.
What changed
What the use case made possible
Priya’s workflow uses example conversations as product-design material. Claude can help generate, critique, or organize possible user-assistant interactions. Those conversations reveal the jobs the product needs to support, the edge cases that matter, and the UI states that should exist. Once the interaction model is clearer, tools like Magic Patterns can turn the flow into an interactive prototype that the team can review and test.
Why this matters
Why this use case is worth studying
This use case is valuable because AI products are often behavior-first, not screen-first. The important design question is not only where the button goes. It is what the assistant should know, say, ask, and do in a messy real interaction. Starting with conversations gives the team something more realistic to design around than a generic assistant mockup.
Use this when
When this pattern applies
Use this pattern when you are designing an AI feature and the behavior is not yet clear. It works especially well for assistants, search experiences, recommendation flows, and any product where the user’s words, context, and trust matter more than the first screen mockup.
