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.
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.
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.
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.
Cues uses 'Visual Context Engineering' to let users communicate intent without complex text prompts. By using a 2D canvas for sketches, graphs, and spatial arrangements of objects, users can express relationships and structure visually, which the AI interprets for more precise outputs.
Product Requirement Documents (PRDs) are often written and then ignored. AI-generated prototypes change this dynamic by serving as powerful internal communication tools. Putting an interactive model in front of engineering and design teams sparks better, more tangible conversations and ideas than a flat document ever could.
For complex features, a 17-page requirements document is inefficient for alignment. An interactive AI-generated prototype allows stakeholders to see and use the product, making it a more effective source of truth for gathering feedback and defining requirements than static documentation.
Instead of providing a vague functional description, feed prototyping AIs a detailed JSON data model first. This separates data from UI generation, forcing the AI to build a more realistic and higher-quality experience around concrete data, avoiding ambiguity and poor assumptions.
AI tools that generate functional UIs from prompts are eliminating the 'language barrier' between marketing, design, and engineering teams. Marketers can now create visual prototypes of what they want instead of writing ambiguous text-based briefs, ensuring alignment and drastically reducing development cycles.
With AI tools like Gemini 3.0 democratizing execution, the ability to generate unique, scroll-stopping ideas and provide strong design references becomes the key differentiator. Good taste and a clear vision now matter more than the technical ability to implement a design from scratch.
The initial fortune-telling app was too generic. By providing simple, natural language feedback like "make it kid-friendly" and "more concrete," the developer iteratively guided the AI to produce a more suitable user experience without writing a single line of code.