Prototyping directly in the production environment makes high-quality interactions achievable without extensive resources. This dissolves the traditional design dilemma of sacrificing quality for speed, allowing teams to build better products faster.

Designing AI experiences in Figma is misleading because it only captures the ideal "golden path." Prototyping in code with live AI models is essential to understand and design for latency, errors, unexpected responses, and the true user "feel" of interacting with an unpredictable system.

Instead of creating multiple static mockups, prompt the AI to build a widget directly into a prototype that allows clicking through different design styles. This provides a live, interactive way to evaluate options within the actual user interface.

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.

Product teams often use placeholder text and duplicate UI components, but users don't provide good feedback on unrealistic designs. A prototype with authentic, varied content—even if the UI is simpler—will elicit far more valuable user feedback because it feels real.

Before using a dedicated AI prototyping tool, run your prompt through Claude.ai first. Its artifact generation provides a quick, lightweight visual of the prompt's output, allowing you to catch errors and refine the prompt without wasting time or credits on a more robust platform.

Historically, resource-intensive prototyping (requiring designers and tools like Figma) was reserved for major features. AI tools reduce prototype creation time to minutes, allowing PMs to de-risk even minor features with user testing and solution discovery, improving the entire product's success rate.

A practical AI workflow for product teams is to screenshot their current application and prompt an AI to clone it with modifications. This allows for rapid visualization of new features and UI changes, creating an efficient feedback loop for product development.

Unlike traditional software, the core of an AI product is its dynamic, often unpredictable output. Static wireframes, even with placeholder text, are mere 'gargoyle rain spouts'—decoration that fails to represent the actual system. You can't validate an AI idea without building and testing the real, content-generating thing.