Figma AI vs Puck AI: Design Tools vs UI Generators

TL;DR

• AI in interfaces started with design help and mockup generation. Now it is moving towards generating UI inside real applications using predefined components and rules.
• Figma AI accelerates visual exploration and layout ideation within the design layer.
• Puck AI generates production-ready UI using existing React components within the application layer.
• The shift is from AI helping teams imagine interfaces to AI helping teams ship scalable, business-aware UI systems.

Introduction

You type a prompt: “Create a SaaS landing page with a hero, pricing, and testimonials.”

In a design tool, the output is a mockup with layout, styles, and reusable components. It helps designers and developers define and validate the UI before development.

In a component-based UI generator, the output is a page built using existing React components from the codebase, which can be used directly in the application and generated by users or systems without requiring new implementation or redeployment.

These represent two layers of AI in UI workflows:

• Design Layer: Generates visual layouts for exploration and validation
• Production UI Layer: Generates UI using predefined components, props, and rules

The difference is not just where AI runs, but what it produces: design artifacts vs executable UI.

This article compares Figma AI and Puck AI as examples of these two approaches.

Two Layers of the Modern Product Stack

Modern product development teams operate across two distinct layers, even if they appear tightly connected.

The first is the design layer. This is where interfaces are explored before they are implemented. Designers and developers work with frames, auto layout, tokens, and reusable components to define structure and visual hierarchy. The output here is a design artifact. It communicates intent, layout decisions, and system patterns. It does not execute inside the application.

The second is the production UI layer. This is where implemented interfaces are executed and used. React components, props, routing, state management, and business rules define how the UI behaves. Here, every element maps to real code, data contracts, and deployment pipelines, and can be used directly by users or systems.

AI systems operate differently across these layers.

• In the design layer, AI assists in visual exploration.
• In the production UI layer, AI generates UI using predefined components and constraints.

What Figma AI Does: Design

Figma AI is a set of AI features integrated into the Figma design environment. It helps designers and developers create layouts, organize components, and generate content during the interface design phase.

Figma AI can also provide code suggestions (such as CSS or component-like structures) through features like Dev Mode. However, this code is generated from design artifacts and does not have awareness of application logic, state, or component contracts.

As a result, the output is mainly used for design exploration and handoff, and still requires implementation and alignment within the actual codebase.

• Layout Generation: Creates structured frames and section layouts from prompts, reducing manual arrangement of design elements.
• Auto Layout Assistance: Applies spacing, alignment, and resizing rules to maintain consistent visual structure across components.
• Design System Alignment: Suggests reusable components and styles to keep designs consistent with established tokens and patterns.
• Wireframe Acceleration: Generates early-stage mockups and placeholders for faster concept validation.
• Content and Copy Suggestions: Proposes contextual text for UI sections to speed up iteration during prototyping.

Figma AI improves speed and clarity during visual exploration. The resulting artifacts still require engineering implementation within the application stack.

What Puck AI Does: Production UI

Puck AI is an AI layer built on top of the Puck visual editor framework. It generates structured user interfaces that map directly to predefined React components and schemas.

Unlike design-centric AI, Puck AI works within the application context and leverages the host’s component configuration, allowing UI output to align with real code and application constraints.

• Component-Based UI Generation: Produces UI by assembling React components defined in the host application rather than generating arbitrary code.
• Schema-Constrained Output: Respects component props, field definitions, and configured layout constraints to ensure the generated UI matches the application’s data model.
• In-Editor Generation: Runs inside the Puck visual editor, enabling designers and developers to refine AI output within the same interface where components are assembled manually.
• Business Context Injection: Allows teams to feed brand guidelines, business rules, or domain constraints into the generation process so that output aligns with both UI standards and application logic.
• Headless Automation API: Exposes APIs for programmatic UI generation, enabling automation of page creation workflows or integration into internal tooling pipelines.

Puck AI generates brand-consistent, production-aware UI that fits directly into a React application’s component architecture.

Figma AI vs Puck AI

Dimension	Figma AI	Puck AI
Primary Output	Design files, frames, mockups	Structured UI assembled from registered React components
Execution Context	Operates inside the Figma design environment	Operates inside your application and visual editor runtime
Constraint Model	Layout, styling, and design system rules	Component schemas, props, and layout configuration
Source of Truth	Design system and visual tokens	React component definitions and field schemas
Workflow Position	Pre-development, before code implementation	Post-deployment, runtime UI composition and authoring workflows
Implementation Alignment	Requires developer translation into code	Uses existing production components
Business Context Handling	Brand consistency and visual guidelines	Brand, business rules, and structured data constraints
Speed Optimization	Accelerates ideation and visual iteration	Accelerates repeatable component based page assembly
Scalability Model	Scales design exploration	Scales production ready UI generation
Automation Capability	Limited to design level generation	Supports headless UI generation outside the editor

When to Use Which

Use Figma AI when:

• Exploring new layout concepts or interaction patterns
• Iterating quickly on wireframes and visual structure
• Validating design direction before engineering effort
• Refining design system usage at the mockup level

Use Puck AI when:

• Generating UI from existing React components
• Producing deterministic, production-ready pages at scale
• Embedding business rules and data into UI generation with tools
• Automating structured page creation within runtime UI composition and live content authoring workflows

Final Verdict

AI in UI workflows started mainly to help with design, like creating mockups and trying different layouts faster. Now it is moving towards generating UI inside real applications using existing components and system rules.

Are you using AI to explore ideas, or to actually ship UI inside your React stack? Explore Puck to build your own visual editor, and try Puck AI to generate production-ready UI using your real components.