Instead of prioritizing a problem and then designing a solution, leading companies build prototypes for multiple problems simultaneously. They then productionize the problem-solution pair that proves most effective through internal testing, a concept called "product shaping."
Resist the temptation to treat AI-generated prototype code as production-ready. Its purpose is discovery—validating ideas and user experiences. The code is not built to be scalable, maintainable, or robust. Let your engineering team translate the validated prototype into production-level code.
An AI prototype is a powerful artifact that details user experience and functional requirements. However, it doesn't replace the Product Requirements Document (PRD). The PRD remains essential for outlining the strategic "why"—market differentiation, user acquisition, and monetization—which a prototype cannot convey.
True AI prototyping mastery isn't about a single tool. It involves a structured progression through 15 distinct skills, from basic prompting (Apprentice) to versioning (Journeyman) and creating fully functional prototypes (Master). This ladder turns "AI slop" into high-craft work.
To ensure AI prototypes match your product's design system, don't just describe the style. Instead, start by prompting the tool to "recreate" a screenshot of your live app. Refine this initial output to create a high-fidelity "baseline" template for all future feature prototypes.
Prototyping a new product from scratch risks creating a generic, "AI slop" design. To avoid this, use "inspiration sourcing": find screenshots from other apps (e.g., on Mobbin) that have the design aesthetic you want, and feed them to the AI as a style reference for specific features.
Move beyond one-on-one interviews for prototype feedback. By prompting an AI tool to integrate analytics platforms like PostHog, you can gather quantitative data at scale. This allows you to track usage, view session replays, and analyze heatmaps, providing robust validation before engineering gets involved.
The industry has not standardized who owns AI prototyping. Three models are emerging: PM-led (leveraging deep customer knowledge), design-led (leveraging craft and speed), and collaborative (PMs and designers working together in the tool). Organizations should choose the model that best fits their team dynamics.
To maximize creative exploration ("diverging"), don't rely on one tool. Run the same open-ended "explore" prompt in several different AI prototyping tools. Each tool's unique system prompts will yield surprisingly different design directions, giving you a wider range of ideas to evaluate.