Accelerating Design System Creation with Generative AI

Speed (and context) matter

The promise of a design system revolves around efficiency, scalability, and consistency. But before teams can reap the benefits, they must navigate three critical phases: Creation, Adoption, and Usage — each requiring significant time and resources.

As a Product Design Manager, I led the initiative to build our design system, working alongside a cross-functional team to define and document reusable components. One of the biggest challenges? Creating documentation that wasn’t just comprehensive — but actually useful in our specific business context.

We didn’t just need generic component guidelines. We needed documentation that:

— Aligned with our B2B SaaS workflows
— Reflected the complexity of our enterprise use cases
— Provided clear, structured guidance for designers & developers

This is where Generative AI entered the equation.

Instead of manually writing one-size-fits-all documentation, we used AI to generate context-aware examples tailored to our platform and industry. AI didn’t just help us speed up the process — it helped us customize it, ensuring that every guideline was business-relevant and immediately actionable.

But before we could even document components effectively, we first had to define them — making strategic decisions about what belonged in our system and how it should be structured.

Curating the Component Library: Balancing Speed and Scalability

As I embarked on the mission of curating our component library, I studied a wide range of style guides and component systems — each with its own levels of complexity, governance, and presentation. It quickly became clear that I had several possible approaches:

1. Start from scratch. Spend months meticulously defining each component, writing exhaustive documentation, illustrating every detail, cross-referencing Google’s Material Design principles, and, ultimately, crafting a bespoke system that would impress even the most discerning design purists. (Estimated time: ~6 months. End result: A beautifully intricate system, but at what cost?)
2. Shortcut the process. Spend an afternoon with a true crime podcast, lift existing guidelines from another platform, and call it a day. (Fast, but completely misaligned with our product needs.)
3. Strike a balance between efficiency and customization. Start with out-of-the-box components (leveraging the truly comprehensive React Material-UI Figma template) and refine through design tokens as the project progressed. (Scalable, efficient, and adaptable to our needs.)

We chose Option 3. The roadmap was months long, but we needed to start building immediately. Instead of delaying progress in pursuit of perfection, we focused on iteration — establishing a solid foundation and refining components as real product needs emerged.

And while the decision wasn’t purely dictated by business constraints, it’s worth noting: this was a B2B SaaS platform, not a consumer-facing site. That distinction mattered. We didn’t need a hyper-polished, pixel-perfect UI with deep branding intricacies. We needed a system that could support complex workflows, enable rapid iteration, and remain scalable for future enhancements.

This pragmatic approach allowed us to move fast without sacrificing quality. However, there was still one major hurdle: documentation. This is where generative AI came into play.

How We Used Generative AI to Speed Up Documentation

With our component library defined, we faced our next major challenge: documentation. Every component needed clear usage guidelines, covering best practices, pitfalls, and alternative approaches. Writing this manually for an entire system would have been a massive undertaking, requiring weeks of effort across our team.

Instead, we turned to generative AI to automate and accelerate the process.

Rather than generating freeform content, I designed a structured prompt to ensure consistency, clarity, and relevancein our documentation. The prompt followed a template that ensured each component entry contained:

• A concise description of what the component does
• Five best practices for its usage
• Five common mistakes to avoid
• Alternative components for different use cases

The Prompt We Used

Here’s the exact prompt we fed into ChatGPT to generate our documentation:

Your task is to craft a detailed style guide document for website design using specific React Material-UI components. The components can be found below, surrounded by square brackets: [Component name(s)].

The style guide should include the following sections for each component:

Description: A brief explanation of what the component is and its purpose.
Do’s: A list of 5 best practices or recommended usages for this component.
Don’ts: A list of 5 common mistakes or practices to avoid when using this component.
Alternatives: Suggestions for other components or approaches to consider if this component might not be the best choice in certain scenarios.

Please follow the structure below for each component:

[Component Name]

Description:
[Provide a brief explanation of the component and its purpose.]

Do’s:
[Best Practice 1]
[Best Practice 2]
[Best Practice 3]
[Best Practice 4]
[Best Practice 5]

Don’ts:
[Common Mistake 1]
[Common Mistake 2]
[Common Mistake 3]
[Common Mistake 4]
[Common Mistake 5]

Alternatives:
[Alternative 1: Explanation]
[Alternative 2: Explanation]
[Alternative 3: Explanation]

Please generate the style guide according to the guidelines above.

By feeding specific component names into this prompt, we were able to generate first drafts of our documentation in minutes rather than hours.

Tailoring AI-Generated Content to Our Platform

Of course, AI-generated content wasn’t a perfect solution out of the box. While the structure and baseline information were solid, the content still needed refinement to align with our platform’s unique needs.

For example, I realized that while generic component documentation existed elsewhere, it wasn’t tailored to our B2B SaaS use case. AI provided a great starting point, but my team still reviewed, edited, and adapted the output to reflect our:

• Business constraints (e.g., compliance considerations for healthcare data)
• Platform-specific patterns (e.g., how forms and modals should behave in our app)
• User expectations (e.g., the need for efficient workflows in high-volume environments)

This approach allowed us to move quickly without sacrificing quality, ensuring our documentation was both comprehensive and relevant.

What Surprised Us About Using AI

Beyond the time savings, we discovered a few unexpected benefits from integrating generative AI into our workflow:

1. Standardization Across Components — The AI-generated documentation followed a uniform structure, ensuring consistency across our design system.
2. Faster Onboarding for Designers & Developers — With clear, structured guidance available immediately, new team members ramped up faster.
3. A Living Documentation System — Since AI made updates faster, we were able to iterate on documentation more frequently, rather than letting it become outdated.

Ultimately, generative AI helped us accelerate the Creation phase of our design system, freeing up valuable time for designing, refining, and ensuring adoption.

Creating Component Examples: Tailoring Documentation to Our Business Needs

This is where Generative AI really accelerated our process. While standard component documentation is helpful, it lacks real-world context. AI allowed us to generate examples that were not just technically correct but also relevant to our platform, industry, and users.

By incorporating details about our platform and business goals, we were able to generate practical, context-aware examples that illustrated how components should be used in real scenarios. Here’s an example of how we structured a customized AI prompt for component documentation:

AI-Generated Component Examples for Our Platform

Using the project details provided below, generate example alert messages for the severities “Error” and “Warning.”

Each alert message should include:

- A title
- A brief description of the issue
- A recommended Material UI component to display the alert

Project Overview:
AutoFleetSync provides automotive companies with a comprehensive solution for managing vehicle fleets, covering everything from maintenance schedules to real-time tracking and analytics.

Key Features:
- Real-Time Vehicle Tracking
- Maintenance Scheduling
- Fuel Consumption Analytics
- Driver Behavior Monitoring

By integrating our industry-specific requirements into the AI prompt, we ensured that the documentation wasn’t just technically accurate — it was also meaningful to the teams using it.

For example, a generic “Error” alert might just explain that something went wrong, but in our system, it needed to explicitly alert fleet managers to urgent vehicle issues. AI-generated examples reflected that context, saving us time while making the documentation immediately useful.

Conclusion: AI as a Force Multiplier for Design Systems

The journey from inception to adoption of a design system is filled with time-intensive, resource-heavy tasks. The Creation phase, in particular, can either enable fast adoption or become a bottleneck. By integrating Generative AI, we expedited documentation, streamlined workflows, and reduced friction between ideation and implementation — all without sacrificing quality.

Looking back, this wasn’t just about speeding up documentation; it was about scaling our efforts strategically. AI allowed us to spend less time on repetitive tasks and more time on the creative, high-value aspects of building a robust design system.

As the design landscape continues to evolve, AI is not a replacement for human expertise, but a force multiplier — a tool that allows teams to work faster, smarter, and with greater focus on innovation. For design leaders, the challenge isn’t just whether to adopt AI, but how to integrate it in ways that truly enhance our craft.