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Is your AI acting like a junior developer? Learn how to provide the right inputs to get high-quality, automated code from your Figma designs.

#1about 5 minutes

The traditional design-to-development handoff process

Designers provide static specifications like CSS properties from tools like Figma for developers to manually implement in code.

#2about 3 minutes

Reframing AI as automated intelligence for developers

AI can be viewed as automated intelligence, which focuses on streamlining developer workflows by automating repetitive processes.

#3about 2 minutes

Understanding core AI concepts like LLMs, agents, and MCPs

Key AI terms are defined, with LLMs as the brain, agents as the body with tools, and MCPs as the middleware connecting them.

#4about 4 minutes

Generating automated tests using Playwright and an MCP

A demonstration shows how to use a Playwright MCP to generate test code from a structured natural language prompt.

#5about 5 minutes

Generating UI components directly from Figma designs

The Figma MCP allows developers to generate UI components, including variations and Storybook stories, directly from a Figma design URL.

#6about 4 minutes

Automating component creation with a defined workflow file

A workflow file can automate a multi-step component generation process, but the output may still require manual refinement and lack component reuse.

#7about 5 minutes

Using structured design data for better code generation

To improve AI-generated code quality, designs must be structured with well-defined properties, variants, and states that can be synced to the codebase.

#8about 5 minutes

Bridging the gap between design and code with Figma Connect

Figma Connect syncs design tokens and component properties, allowing developers to see live code documentation within Figma and maintain consistency.

#9about 7 minutes

Maintaining human oversight in AI-assisted development

AI tools augment workflows but require human supervision and a fundamental understanding of the code being generated to prevent errors and ensure quality.

#10about 6 minutes

The risks of over-automation and losing core skills

Relying too heavily on automation can lead to a loss of fundamental knowledge, making it difficult to debug issues or understand the 'why' behind the code.