Vercel's Pranati Perry explains that tools like V0 occupy a new space between static design (Figma) and development. They enable designers and PMs to create interactive prototypes that better communicate intent, supplement PRDs, and explore dynamic states without requiring full engineering resources.

AI-powered "vibe coding" is reversing the design workflow. Instead of starting in Figma, designers now build functional prototypes directly with code-generating tools. Figma has shifted from being the first step (exploration) to the last step (fine-tuning the final 20% of pixel-perfect details).

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.

When iterating on a Gemini 3.0-generated app, the host uses the annotation feature to draw directly on the preview to request changes. This visual feedback loop allows for more precise and context-specific design adjustments compared to relying solely on ambiguous text descriptions.

The handoff between AI generation and manual refinement is a major friction point. Tools like Subframe solve this by allowing users to seamlessly switch between an 'Ask AI' mode for generative tasks and a 'Design' mode for manual, Figma-like adjustments on the same canvas.

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 creating static mockups in Figma, Cursor's design team prototypes directly in their AI code editor. This allows them to interact with the "life states of the app" and get a more realistic feel for the product, bridging the gap between design and engineering.

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.

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.