AI ideas tend to arrive wrapped in infrastructure: a knowledge base, an orchestration layer, an evaluation pipeline, a full application. That can make a simple question feel like a six-month commitment.
A prototype should isolate the uncertainty. If the question is whether a model can turn a messy intake call into a useful project brief, build exactly enough to test that transformation with real examples.
Make the riskiest claim tangible
Write down the claim that has to be true for the idea to matter. Then create the smallest experience that lets someone react to that claim. Often the first prototype can use manually prepared context, a narrow interface, and a human behind the curtain.
This is not cutting corners. It is buying evidence before buying infrastructure.
Build what the learning earns
A strong response to the prototype tells you what to automate next. A weak response saves you from polishing the wrong thing. Both outcomes are progress when the experiment is designed well.
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