Prototyping is how you find out whether your business and design decisions hold before you build.
With a conventional feature, two questions drive that process: do users want this, and can they use it? Every user gets the experience you designed. When the prototype reveals a problem, you can trace it to a decision your team made.
With an AI feature, you’ll need to address additional design concerns because the output is generated rather than designed. And it is generated differently across users and contexts. You do not control what comes out. You design the conditions under which the model operates.
The Three Additional Questions
Does the input design give the model what it needs to produce a useful response? A user submitting a support request might type three words. Whether the model receives those three words, or those three words plus account history and product context, is an input design decision.
Does the model produce useful output reliably across the full range of inputs? The same input can produce different outputs across users. Inputs your team did not anticipate can produce outputs that are wrong, incomplete, or inaccurate. Those are not edge cases, but inputs that reveal whether the design holds.
Does how the model’s response is presented help users evaluate what they received well enough to act on it? The model returns something: a recommendation, a summary, a table, an image, or a generated draft. How that appears to the user is a design decision. It determines whether the user can judge the quality of what the model produced.
These three questions are not independent. A poorly scoped input does not just affect what the model receives. It affects what the model produces, which affects whether users can make sense of it. Traditionally, when users do not trust the output, the instinct is to fix how it is presented. But with AI, the problem may be that the model is producing output that is wrong or unreliable.
What User Reactions Tell You
User reactions during prototyping point to where business context or design decisions are not holding up. A user who asks “what is it using to do this?” is not asking for a technical explanation. They are telling you the AI’s basis for its output is not visible enough to trust. A user who asks “how do I know what to include?” is telling you the input design is not giving them enough guidance. These are not blockers. They are signals about which decision needs revisiting.
Each reaction points to something. Acting on what each iteration reveals is what moves the prototype closer to a solution users trust enough to adopt.
Why Prototyping Iteratively Matters More for AI Features
With a conventional feature, a thoughtful design review catches most problems before a prototype is built. The behavior is specified and the output is predictable.
With an AI feature, a design review cannot do the same work. The output varies across users, contexts, and use cases in ways that are only visible when a live model meets real inputs. A decision that looks sound in review can break in ways that cannot be anticipated without seeing the model respond to the full range of what users actually bring to it. More time in prototyping means less time correcting decisions that should have been caught before development began.
