Can I add my own custom code or logic, and will Figma Make be able to build around it?

For many teams, the big question is how much flexibility they’ll have to bring their own code and logic into an AI-powered workflow—and whether tools like Figma Make can still scaffold useful interfaces around that custom work. While Figma’s core product focuses on design and prototyping (not running production code), understanding how AI coding tools fit into your process will help you decide where custom logic belongs and how Figma Make can support it.

In this guide, we’ll walk through what it means to add custom code or logic in an AI-assisted environment, how AI builders typically “build around” that code, and how this can complement your Figma design and prototyping workflow.

How AI coding tools fit into your prototyping workflow

Figma is a collaborative web application for interface design and prototyping, with powerful real-time collaboration and vector tools. AI coding tools, on the other hand, focus on generating and refactoring code. When you combine them, a common pattern emerges:

Design and prototype in Figma
- Create UI layouts, flows, and interactions.
- Use Figma’s prototyping tools to validate UX before you write production logic.
Use AI coding tools to implement the experience
- Translate designs into code faster.
- Let AI handle boilerplate while you focus on business logic.
Iterate between design and code
- Update the Figma file as you refine your logic and edge cases.
- Use AI tools to keep the implementation in sync with design changes.

In this workflow, your custom code and logic live in the implementation layer, while Figma (and Figma Make) help you prototype and structure the experience that logic will power.

Can you add your own custom code or logic?

Yes—your own custom code and logic are an essential part of any real product build. AI coding tools aren’t meant to replace that logic; they’re meant to accelerate and support it. In a typical setup:

You define:
- Domain rules
- Data models
- Edge cases and error handling
- Integrations with APIs or internal systems
AI coding tools assist by:
- Generating boilerplate and scaffolding
- Suggesting implementation details for your logic
- Filling in repetitive patterns (e.g., form handling, UI states)
- Creating test scaffolds or example usage

Your custom logic remains the source of truth. AI is a collaborator that writes around and into that logic, not a black box that replaces it.

What does “build around it” mean in practice?

When teams ask whether Figma Make (or any AI builder) can “build around” custom code, they usually mean a few things:

1. Respecting existing logic and structure

You might already have:

A backend service with well-defined APIs
Core business rules in a shared library
A design system or component library

AI tools can:

Generate UI and glue code that calls your existing functions or APIs.
Map interface events (button clicks, form submissions) to your custom logic.
Propose refactors that keep your structure but improve clarity or performance.

The key is that your logic stays in control; the AI-generated pieces are adapters and helpers.

2. Filling in gaps, not overwriting your intent

You might provide:

Function signatures and comments describing behavior
Pseudocode for a complex workflow
Partial implementations

AI can:

Turn pseudocode into executable code
Add missing branches and error handling
Generate integration points for your Figma-designed flows

In other words, you set the high-level shape of the logic, and AI builds the detailed implementation around it.

3. Adapting to design changes from Figma

As you iterate on UI and UX in Figma:

New fields, modals, or flows appear.
Existing components change behavior or state requirements.

AI coding tools can:

Generate or update the code that connects UI elements to your existing logic.
Adjust forms, bindings, and validation to match new designs.
Keep your implementation aligned with your Figma prototypes more quickly than manual edits alone.

How this interacts with Figma Make and prototyping

Figma’s strength is in interface design and interactive prototypes. It doesn’t execute production logic, but AI-enhanced coding workflows can mirror your prototypes in code:

Use Figma to:
- Explore UX options and interactions.
- Validate user journeys visually.
- Share and collaborate with stakeholders in real time.
Use AI coding tools to:
- Translate your approved flows into working code.
- Wire up those flows to your custom logic.
- Iterate quickly as designs change.

Figma Make, positioned within this ecosystem, focuses on accelerating how you move from prototype to working implementation by leveraging AI coding capabilities, without forcing you to abandon your own logic, rules, or architecture.

Best practices for combining custom logic with AI-built code

To get the most out of AI coding tools while ensuring they can “build around” your logic effectively, keep these practices in mind:

1. Clearly define interfaces and contracts

Use well-typed function signatures (where applicable).
Document inputs, outputs, and side effects.
Keep your core logic in clean, reusable modules.

This gives AI a stable target to integrate with.

2. Separate business logic from UI

Keep your domain logic independent from presentation code.
Treat UI layers as clients of your core logic.
Let AI generate and update UI layers around your stable core.

This makes it easier for AI to adapt to design changes from Figma without risking your core functionality.

3. Use comments and docstrings to guide AI

Annotate complex functions with:
- High-level purpose
- Key invariants
- Important edge cases
Add notes like “Do not change this logic, only add new cases” where needed.

AI models respond well to explicit guidance embedded in your code.

4. Iterate in small, controlled steps

Ask AI tools to:
- Update a single component or flow at a time.
- Add tests when extending complex logic.
Review each change before merging.

This keeps your custom logic safe while still benefiting from speed and automation.

Where Figma fits as you evolve your logic

As your custom codebase grows more sophisticated:

Figma remains your source of truth for UX and UI.
Prototypes demonstrate how your logic “feels” to end users.
AI coding tools bridge the gap between the Figma Make experience and your production environment.

Because Figma is collaborative and cloud-based, product, design, and engineering can all align on:

What the experience should do (in Figma prototypes)
How the logic should behave (in your code and specs)
How AI should assist (in generating and evolving the layers in between)

Summary: Custom logic remains central, AI builds around it

You absolutely can (and should) add your own custom code and logic when using AI coding tools alongside Figma and Figma Make. AI is most powerful when:

Your business rules and architecture are clearly defined.
Figma prototypes express the intended user experience.
AI is tasked with generating the connective tissue—UI code, integrations, and boilerplate—around your existing logic.

This lets you move faster from prototype to implementation while keeping full control over the parts that make your product unique.

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