In partnership with

Parity has entered the chat

When we talk about Figma and code closeness, it’s often referred to as design and code parity. I’ll use this term frequently, so let’s define it:

Parity means ensuring that design components in Figma closely match their coded versions.

However, there are inherent limitations to achieving full parity which I’ll explain below. Let’s get started…

Want to get the most out of ChatGPT?

ChatGPT is a superpower if you know how to use it correctly.

Discover how HubSpot's guide to AI can elevate both your productivity and creativity to get more things done.

Learn to automate tasks, enhance decision-making, and foster innovation with the power of AI.

Download the free guide with actionable prompts and tips to make your work more productive.

Table of Contents

• Why we can’t get as close as we want

• Proposal: Treat parity as a spectrum

• Levels of parity

  • 🟠 Aspirational (low parity)

  • 🔷 Analogous (moderate parity)

  • 💜 High parity

• How to decide the right level of parity

Why we can’t get as close as we want

According to a LinkedIn poll I conducted, 66% of respondents want their design and code components to be as close as possible. However, a few factors make this difficult:

• Design and code exist in different environments – maintaining synchronization requires effort

• Figma lacks certain coding features – accessibility attributes, native state functions, and dynamic content are not easily represented

• Structural differences – Figma’s Auto Layout is not the same as CSS Flexbox, and code-specific definitions like React children can’t be replicated without complex solutions

Example: Parity through translation

Some tools, like Framer, have achieved true design and code parity by introducing a translation layer that directly connects the two. However, most teams don’t have this luxury, so they rely on designers and engineers to manually keep components aligned.

Proposal: Treat parity as a spectrum

Since true parity isn’t feasible for most (yet), we should rethink how we define it. Instead of a strict boolean state (parity vs. no parity), let’s treat it as a spectrum of exactness with the ultimate goal of achieving high parity where possible.

Levels of parity

Your Figma library will likely contain a mixture parity levels dependent on the nature of each component. Let’s break them down:

🟠 Aspirational (low parity)

Something that is either not built yet or purposefully disconnected from the coded equivalent.

🌟 Where it makes sense: Aspirational Figma components and libraries are great to demonstrate a UI wish list, blue sky vision or a new visual direction. Handing over something aspirational to an engineer can be risky, as the cost to build would be high.

💡 My advice: In the world where generative AI is very close to being useful for actual product design, having little to no connection between your design and code is a risk, especially for larger teams.

📓 Use cases/examples: New brand vision, something to be built, when the team has low resources.

🔷 Analogous (moderate parity)

Most Figma libraries and components fall into this category, where aspects of the component align but not everything.

🌟 Where it makes sense: For larger components that involve more structure it’s best to have some similarities but don’t sweat making it perfectly 1:1 as it can add complexity for the designer to use.

💡 My advice: Try to mimic the general layout of these larger components along with their subcomponents, so the structure stays the same, making iterations easier in the future. Try to avoid any solutions that require a designer to click to drill into component instances to change their props.

📓 Use cases/examples: Modal dialogs, Cards, Pages, Side navigation, Menus

💜 High parity

This is the gold standard most design systems aim for.

🌟 Where it makes sense: Most foundations and common interactive components are super important to have high parity due to their frequency of use.

💡 My advice: When components are heavily used for composition it’s important they have the highest parity in your system, especially the smaller, more foundational elements. For example, if a design token is called color/text/main in Figma but colour.text in code, this is low parity. It may seem trivial, but effort should be made to keep these exact.

📓 Use cases/examples: Design tokens, Icons, Type, Button, Dropdown, Textfield

How to decide the right level of parity

If you’re unsure what level of parity makes sense for your team, use the three C’s framework:

Capability

Is full parity even possible? Some components, like those relying on React children, are harder to replicate.

Complexity

Does enforcing parity make the design tool harder to use? If designers struggle with deeply nested instances, parity may not be worth the cost.

Company

Is maintaining parity the best use of resources? Smaller teams with fewer engineers might prioritize high parity for only the most critical components.

✅ Self-assessment:

• Can this component be replicated accurately in Figma? (Capability)

• Will high parity negatively impact usability or performance? (Complexity)

• Does our team have the resources to maintain it? (Company)

If you answer "no" to any of these, consider a lower level of parity.

What’s next?

In Part Two, I’ll share practical strategies to increase parity in your design system, including:

• Matching structure and layout

• Creating connections between design and code

• Improving overall parity levels

Stay tuned! 🚀

🤙 Any feedback is much appreciated