How can I use AI to create design tokens?

2024: Teaching AI to Design Like a Pro

What Are Design Tokens and Why Do They Matter?

You're an amateur designer trying to up your game but don't know where to start. Creating a visually cohesive design system seems impossible without the right tools. But what if AI could do the heavy lifting? In this article, we'll explore how to leverage AI to effortlessly generate design tokens tailored to your brand. From colors and typography to spacing and effects, we'll demystify the process so you can pump out professional designs like a pro. With the right prompts and training data, even the least artistic among us can curate a custom visual language worthy of Dribbble. So plug in your tablet and get ready to teach those neural networks a thing or two about good design. You'll be cranking out gorgeous interfaces and branding materials before you know it.

The Role of AI in Automating Design Systems

Design tokens are the visual design atoms of your UI. They represent things like colors, fonts, sizes, and spacing that make up your design system. By defining and documenting these tokens, you create a single source of truth for your designers and developers.

A Common Language

Design tokens give your team a shared vocabulary to discuss UI designs. Instead of saying "make that text bigger" or "use the blue we used over there", you can refer to specific tokens like "text-lg" or "color-primary". This makes feedback and revisions much more constructive.

Consistency Across Platforms

By mapping your design tokens to actual CSS values, you ensure visual consistency across platforms. The "color-primary" token will output the correct hex code for web, the right UIColor for iOS, and so on. Your product will look cohesive no matter what device a customer uses.


As your design system grows in complexity, tokens help keep things manageable. You can define tokens for spacing, typography, color, etc. and compose them in different ways. Tweaking a single token updates it everywhere it's used. This modular approach is key to building a scalable design system.


When done right, design tokens can help make your UI more accessible. By defining a fixed set of spacing tokens, you ensure text and interactive elements are properly spaced out. Color tokens can be mapped to AA color contrast ratios. And by using tokens for font sizes, you avoid uncomfortable "in-between" sizes.

In the end, design tokens save time and promote quality in UI development. They turn design into a shared, evolving process that entire teams can contribute to. By adopting a tokens approach, you'll be well on your way to building a cohesive design system.

How Can We Teach AI Fundamental Design Principles?

AI has the potential to help automate design systems by generating design tokens. What are design tokens? Design tokens are the visual design atoms of a digital product - things like colors, fonts, spacing, etc. They ensure consistency across all platforms and devices. Currently, design tokens are defined manually by designers which can be tedious and time-consuming. This is where AI comes in.

AI can analyze existing design systems and style guides to generate design tokens automatically. AI models can detect colors, fonts, and spacing used and turn them into reusable design tokens. This can save designers a ton of time and ensure consistency from the get-go. Some companies are already using AI to generate design tokens, with models analyzing UI elements, extracting colors and fonts, and converting them into design tokens.

AI limitations. AI isn't perfect, though, and still needs human designers. AI may misunderstand some UI elements or make incorrect guesses for design tokens. Designers will need to review, tweak and approve the design tokens generated by AI. AI also can't replicate the creativity and intuition human designers have. AI is best suited to handle repetitive, mundane tasks, freeing up designers' time for higher-level thinking.

The combination of human designers and AI is powerful. AI does the tedious work of analyzing UIs and generating initial design tokens, then human designers review, refine and approve. This hybrid approach allows designers to focus on the creative aspects of their work rather than monotonous tasks like documenting design tokens. The future is AI and human designers, working together in harmony.

Designing a Dataset to Train an AI on Design Elements

Design is an art form that requires creativity, intuition, and an understanding of visual aesthetics. Teaching AI these fundamental principles will be crucial to developing its design skills.

Focus on Composition

Composition refers to how elements are arranged and organized in a design. Teach AI about symmetry, balance, alignment, proximity, repetition, and flow. Have it analyze examples of good composition in UI, graphic, and visual designs. AI can then start to identify the compositional patterns that lead to pleasing, impactful designs.

Study Color Theory

Color is a key component of design. Educate AI on concepts like complementary colors, color harmony, and color psychology. Explain how different hues can evoke different emotions. Have AI study the use of color in designs it finds most visually appealing and impactful. With enough examples, it can determine the most effective ways to combine and apply color.

Understand Space

An understanding of negative and positive space is essential for good design. Negative space refers to empty areas around key elements, while positive space contains the main focal points. Teach AI how manipulating space can create emphasis, balance, and visual flow in a design. Show it examples of designs that make highly effective use of space.

Cultivate Creativity

While AI can learn fundamentals, design also requires creativity and original thinking. Expose AI to unusual design examples and have it generate unique concepts. Encourage unexpected combinations of elements, shapes, images, and more. The more AI practices, the more it can cultivate its own creative design sense.

With knowledge of principles like composition, color theory, use of space, and creativity, AI will have a solid foundation for crafting beautiful designs. By studying human designers and practices, AI can gain an intuition for what makes an impactful, visually stunning end result. While AI may never match human creativity, it can certainly develop a more refined design sense.

Developing AI Models to Generate Color Palettes and Typography

Designing a comprehensive dataset to train an AI to generate design elements requires curating a diverse set of examples. Focus on gathering a large volume of design assets in a variety of styles.

Select a Design Discipline

Pick a specific area of design to focus on, such as UI icons, color palettes, typography, or illustrations. Gather at least 1,000 examples in that discipline to give the AI a broad range of inspiration. For example, collect 1,000 unique UI icon designs or 1,000 color palettes used on popular websites.

Include a Range of Styles

Include designs in a variety of styles, from minimal to ornate, retro to futuristic, vibrant to muted. The more styles in your dataset, the more versatile the AI's outputs will be. If training an AI to generate color palettes, include palettes in styles like pastel, neon, earthy, metallic, and monochromatic.

Add Contextual Details

In addition to design examples themselves, include details about the context in which they're used. For a typography dataset, note information like the genre of content the typeface pairs well with, its readability for digital or print, and its cultural associations. These additional details will help guide the AI to generate designs suitable for specific needs.

Keep Your Dataset Current

Update your dataset regularly with new examples to keep the AI generating on-trend designs. A dataset with mostly examples from 5-10 years ago will produce outdated designs. Stay on top of current styles by frequently searching websites, design galleries, and social media platforms like Dribbble and Behance for inspirations to add to your dataset.

With an extensive, well-curated dataset, you'll have everything you need to train an AI to design like a pro. Keep in mind that the larger and more diverse your dataset, the more skilled the AI will become at generating a wide range of innovative designs. Update frequently, and you'll have an AI that can design for both current and future trends.

Testing and Iterating - Do the AI Designs Meet Brand Guidelines?

Training Data

To develop AI models that can generate effective color palettes and typography, you need to feed them a large amount of data to learn from. Gather examples of color palettes, font pairings, and typography styles from design inspiration websites, brand style guides, and award-winning designs. The more examples the AI has to analyze, the more sophisticated its outputs can become.

Choosing an Algorithm

There are a few machine learning algorithms well-suited for generating design elements. Generative adversarial networks (GANs) can create new examples that mimic a dataset. Variational autoencoders (VAEs) can learn latent representations of data and generate new examples. These algorithms allow the AI to understand the nuances of color and type at a high level.

Evaluating the Outputs

Once you've trained your AI models, evaluate the color palettes, font pairings, and typography they generate. Do they demonstrate an understanding of design principles like color theory, typographic hierarchy, and visual balance? Do the outputs feel cohesive and purposeful, or random and disjointed? Look for any biases or limitations in the training data that are reflected in the AI's generation.

Improving the Models

Based on your evaluation, make improvements to the AI models. You may need to provide more examples in areas where the generation seems lacking. Tweak the algorithms and retrain the models.

Using the Models

When the AI can generate high-quality design elements that meet your needs, put the models to work. Have the AI instantly generate color palettes and typography for new web pages, mobile apps, print materials, packaging, and more. The AI can take the hassle out of starting new design projects and accelerate your workflow.

With time and effort, AI models can become quite skilled at crafting color palettes, choosing complementary fonts, and applying typography in a visually appealing way. Keep providing data and feedback, and your AI will be designing like a pro in no time!

Building a Library of Reusable Design Tokens

Reviewing the Designs

Once your AI has generated some design options, it’s time to review them and provide feedback. Compare the designs to your company’s brand guidelines to make sure they align with the appropriate colors, fonts, and style. If not, you’ll need to retrain the AI model with the correct brand assets and style references.

Evaluating the Options

Look at the different designs the AI produced. Do any stand out as particularly on-brand or visually compelling? If so, that’s a good sign your AI is learning. For other options that miss the mark, determine how they can be improved. Maybe the color palette needs refinement or the layout isn’t quite right. Provide specific notes on what you like and don’t like about each design.

Improving and Retraining

Based on your feedback, make tweaks to the AI model and retrain it with the updated information. For example, you might need to add constraints around color selection or provide new examples of on-brand designs. Retrain the model and have it generate a new set of options. Repeat this process, evaluating and retraining until the designs meet your needs.

While AI will get better at design over time, human oversight is still critical – at least for now. Reviewing, evaluating and retraining your AI model is the only way to ensure it produces options that align with your brand standards and meet the level of quality you expect. With each iteration, the designs should improve and become more on-brand. If done well, you'll get to a point where the AI can generate designs autonomously that need only minor refinements or none at all. But as with any AI system, always double check its work and never fully hand over the reins.

Integrating AI-Generated Design Tokens Into Design Systems

To build a library of design tokens for your AI system, you’ll first need to define what exactly comprises a “design token.” Think of these as the visual attributes that make up the look and feel of your product's interface. Things like:


Specify the hex codes or color names for your primary and secondary brand colors, as well as any supporting or accent colors. For example, you might define tokens like:

- $primary-color: #04a4cc;

- $secondary-color: #ff7d11;

- $accent-color: #ecd148;


Define the font stacks, sizes, and weights for headings, paragraphs, buttons, and any other text elements. For example:

- $header-font: 'Work Sans', sans-serif;

- $header-size: 2rem;

- $body-font: 'Merriweather', serif;

- $body-size: 1rem;


Set standard margins, paddings, and line heights to establish a consistent vertical rhythm. Tokens might look like:

- $margin: 1rem;

- $padding: 0.5rem;

- $line-height: 1.5;


Choose the border radii for buttons, cards, images, and other elements with rounded corners. For example:

- $button-radius: 0.25rem;

- $card-radius: 0.5rem;

Establishing these foundational design tokens will give your AI a basic visual language to work with. You can then build more complex tokens on top of these primitives and create stylesheets to apply the tokens to your interface. With time, you'll develop an extensive library of reusable components to draw from, all derived from your initial set of design tokens.

2024 and Beyond - FAQs on AI and the Future of Design

Design systems help create cohesive user experiences by standardizing design elements like colors, fonts, spacing, etc. AI can help generate some of these design tokens to provide a good starting point. You'll want to review and tweak the AI's suggestions to align with your brand guidelines before integrating them into your design system.

To begin, feed your existing design system tokens and brand guidelines into the AI model. The AI will analyze these to determine the rationale behind your design choices and patterns. It can then generate additional tokens in a similar style. For example, if your system currently has a primary blue color, the AI may suggest secondary and tertiary blues to complement it. Or if you specify preferred font sizes for headings, it could suggest sizes for subheadings and body text.

Review the AI's suggestions to ensure they meet your standards for accessibility, inclusiveness, and user experience. You may need to tweak colors for sufficient contrast or adjust font sizes for optimal readability on various screen sizes. Only accept suggestions that strengthen your design system.

Once you've curated the AI's recommendations, integrate them into your design system and update any relevant design documentation. Be sure to note which tokens were generated by AI to inform future review processes. Your design system should remain a living document, open to refinement as your brand and user needs evolve.

Periodically re-run the AI model with your updated design system to continue enhancing it. The more data you provide, the better the AI will get at suggesting coherent and compelling new design tokens. With each iteration, the AI can generate innovative options you may not have considered, helping you establish a robust and forward-thinking system.

Using AI to generate design tokens helps take the guesswork out of expansion and ensures new additions feel cohesive. But human judgment is still critical to build a thoughtful design system aligned with your brand vision. With a combination of human and AI collaboration, you can create a system poised for long term success.