ai-prototyping
AI Prototyping
AI app builders, AI design tools and AI coding assistants turn an idea into a clickable or working prototype in hours. That changes the economics of discovery and adds three failure modes: prototyping what should have been specified, mistaking a polished demo for validation, and shipping prototype code by accident.
The agent runs the whole loop: decide whether to prototype, pick the fidelity rung, brief the generation tool, test with real users against pre-set thresholds, and hand off with everything a prototype cannot carry. Two stdlib tools gate the steps teams skip most: untestable hypotheses and incomplete handoffs.
When to use
- A team wants to "just build it with AI" and someone must decide if that is the right move
- Choosing between a clickable mock, a working prototype on synthetic data, or a spec
- Writing the brief for an AI prototyping tool so the output is testable
- Planning a prototype test: tasks, participants, success and kill criteria
- Handing a validated prototype to engineering without it becoming production code by default
- Setting governance for prototype tools: data, security, IP, accessibility
When NOT to use: the dominant risk is viability (pricing, margin, legal) - model it; the solution is already known (parity feature, regulatory requirement) - write the spec; there is no evidence the problem exists - interview users first; you need a rate with a confidence interval - run a powered quantitative test.