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.

A powerful, free workflow combines two Google tools. Use Stitch for divergent, visual ideation by generating multiple design variations from a prompt or screenshot. Then, export the preferred design directly to Google AI Studio to instantly convert it into an interactive, code-based prototype.

When using "vibe-coding" tools, feed changes one at a time, such as typography, then a header image, then a specific feature. A single, long list of desired changes can confuse the AI and lead to poor results. This step-by-step process of iteration and refinement yields a better final product.

Before writing code, you can string together hyperlinked screenshots in a design tool like Figma. This creates a 'hacky' prototype that feels like a fully built app to potential customers, allowing for rapid, low-cost user testing and validation.

Instead of writing detailed specs, product teams at Google use AI Studio to build functional prototypes. They provide a screenshot of an existing UI and prompt the AI to clone it while adding new features, dramatically accelerating the product exploration and innovation cycle.

Move beyond basic AI prototyping by exporting your design system into a machine-readable format like JSON. By feeding this into an AI agent, you can generate high-fidelity, on-brand components and code that engineers can use directly, dramatically accelerating the path from idea to implementation.

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.

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.

To avoid generic, 'purple AI slop' UIs, create a custom design system for your AI tool. Use 'reverse prompting': feed an LLM like ChatGPT screenshots of a target app (e.g., Uber) and ask it to extrapolate the foundational design system (colors, typography). Use this output as a custom instruction.