How A Component-first Approach Fixes Figma-to-code

You’ve astir apt tried 1 of nan Figma-to-code devices that committedness to move your designs into moving React aliases HTML pinch a azygous click. They look for illustration nan cleanable shortcut, offering a measurement to skip repetitive layout activity and move straight from creation to product.

But velocity often comes astatine nan costs of structure. The exported codification looks correct successful a preview, yet beneath nan surface, it’s static, repetitive and difficult to maintain. Styles are hard-coded, components are flattened into generic <div> tags and thing connects backmost to your creation system. The consequence is codification that looks correct but can’t adapt, standard aliases beryllium reused by your team.

Let’s research why that happens and really to hole it. You’ll spot what Figma-to-code devices really generate, why their output isn’t fresh for accumulation and really a composable architecture helps you move creation exports into modular, production-ready components.

What Figma-to-Code Tools Generate

The main problem pinch Figma-to-code devices is that they transcript nan ocular layout of a creation alternatively than its underlying structure. They dainty each constituent arsenic a unsocial style alternatively of recognizing patterns aliases reusable components.

To spot really astir Figma-to-code devices behave successful practice, commencement pinch a elemental dashboard creation from nan Figma Community, specified arsenic this minimal finance dashboard. Export it to codification utilizing immoderate celebrated Figma-to-code plugin that claims to make responsive code.

Within seconds, you’ll get a moving preview that looks impressive. The layout is aligned, matter is rendered neatly and nan buttons look to respond to interaction.

But upon inspecting nan generated code, nan limitations go obvious. The layout isn’t responsive and elements that should beryllium interactive, specified arsenic buttons aliases pagination, are rendered arsenic generic <div> elements.

For example, it generated:

Instead of thing person to:

Even Figma components that look interactive, specified arsenic selects aliases pagination controls, incorporate nary logic. They’re styled placeholders pinch nary behaviour aliases authorities management.

This highlights a deeper rumor of these devices replicating much of nan quality than nan architecture. They tin reproduce nan aboveground of a UI, but they don’t seizure its composition, behaviour aliases intent.

Why Figma-Generated Code Fails successful Production

Beyond building and responsiveness, generated codification besides fails successful ways that make collaboration and semipermanent usage difficult. They include:

It’s Not Connected to Your Design System

Design systems beryllium to enforce consistency. They springiness you tokens for spacing, typography and colour and they specify reusable components for illustration buttons, cards and modals. Figma-to-code devices disregard each of this. In nan exported dashboard, nary of nan styles were linked to creation tokens aliases strategy variables. The “New deposit” button, for example, wasn’t mapped to immoderate existing component; instead, it was rebuilt from scratch. Over time, this attack tin create a protector strategy of mismatched components that drift distant from your existent creation system.

No Version Tracking

When you export codification from a Figma-to-code plugin, it comes retired arsenic a azygous record dump. There is nary history of really nan creation evolved aliases who changed what. That makes it astir intolerable to trace a UI rumor backmost to its source. In accumulation environments, teams trust connected git history and creation versioning to collaborate safely. Without that link, each export becomes a stiff snapshot that cannot beryllium rolled backmost aliases compared. Developers often extremity up deleting nan output and starting caller pinch manual coding to debar nan mess.

It’s Hard For Another Developer to Pick Up

Even if you understand nan generated codification yourself, different developer joining nan task mightiness not. The export provides nary clear constituent boundaries, nary spot (prop) definitions and nary mentation of really it is meant to beryllium used. There is nary inline archiving to show expected states aliases variations. Without this context, a caller developer has to reverse technologist some nan creation intent and nan quirks of nan codification earlier making moreover a mini change. In a squad setting, this deficiency of clarity quickly becomes a productivity killer.

These limitations are why astir teams extremity up discarding nan codification generated by Figma-to-code devices altogether. But what if nan workflow started pinch composability alternatively of fixed markup?

How to Convert Figma Designs To Production-Ready Code

To move creation exports into maintainable code, commencement by importing nan aforesaid Figma dashboard utilized earlier into Hope AI. The instrumentality will first analyse nan layout and hierarchy, mapping nan design into a constituent character that highlights reusable patterns specified arsenic buttons, cards and shape inputs.

This pre-generation measurement is wherever building takes shape. You reappraisal nan projected architecture, corroborate nan relationships betwixt components and set naming aliases grouping wherever necessary. This process reverses nan accustomed design-to-code workflow, ensuring that building leads and codification follows.

Once nan building is approved, Hope AI generates nan components and builds an interactive demo. Each constituent uses due HTML semantics. Buttons are defined arsenic <button> elements, selects are <select> and tables travel correct markup conventions. The generated UI besides includes basal interactivity. Pagination, shape inputs and sorting behaviors are each functional.

If akin components already beryllium anyplace successful your task aliases company, Hope AI detects them and reuses those alternatively of duplicating caller ones. This keeps your creation strategy cleanable and consistent. Behind nan scenes, nan codification is organized arsenic Bit components, each pinch props, tests, a README and a unrecorded creation example. You tin besides widen these to people circumstantial components successful your creation system.

The generated components are fresh for versioning and tin beryllium shared crossed projects aliases teams. Each 1 is independent and discoverable successful your constituent registry, which makes collaboration and scaling overmuch easier.

Beyond semantics and structure, Hope AI’s composable architecture is moreover much powerful successful accumulation workflows.

Why Bit Cloud AI-Powered Workflow Scales In Production

The existent worth of a composable workflow shows up successful really teams build, support and standard applications. Some areas include:

• Reuse crossed projects: Components are packaged arsenic versioned units that tin beryllium imported into immoderate project. Your squad tin debar rebuilding nan aforesaid fastener aliases paper aggregate times, ensuring accordant behaviour and faster delivery.
• Design-to-development feedback loop: Since components unrecorded successful a shared registry, designers tin position existing components earlier creating caller patterns. This shortens handoff cycles, reduces plagiarism and keeps creation and codification aligned.
• Version history and traceability: Every constituent comes pinch type history. You tin spot erstwhile and why thing changed and rotation backmost safely. This replaces nan fixed and one-off quality of Figma exports pinch a collaborative workflow.
• Production-ready from nan start: The generated components usage semantic HTML, see tests and meet accessibility standards. You tin vessel them straight alternatively of rewriting aliases debugging generated code.

The Hope AI workflow demonstrates that design-to-code tin activity successful production, but only if nan workflow respects really teams build and support software.

Wrapping Up

Most Figma-to-code devices export fixed markup that mirrors nan creation but fails successful production. The output is rigid, unstructured and disconnected from your system, which is why teams often discard it and rebuild from scratch. A composable workflow specified arsenic Bit’s Hope AI takes a different path. Designs are surgery down into reusable components pinch props, semantic HTML, tests and type history. Instead of throwaway code, you get building blocks that fresh people into a existent codebase and tin standard pinch your product.

Engineering judgement will ever beryllium needed, but starting pinch production-ready components gives teams a superior caput start. It reduces duplication, improves collaboration and creates a shared root of truth betwixt creation and development.

Want to spot this workflow successful action? Run a creation done Hope AI and get system-ready components you tin version, stock and reuse crossed projects.

Group Created pinch Sketch.