AI in MarkupGen: From Code Generation to Scoring

AI in MarkupGen isn't a single feature — it runs through all three stages of the workflow: building code from a design, judging the quality of that output, and suggesting how to improve it when the result doesn't quite match. This post walks through each layer, and why they're built to support a developer's judgment rather than replace it.

Building code from the design's structure

At the first stage, AI doesn't "redraw" the design from a screenshot — it reads the actual structure the Figma plugin sent over: Auto Layout, spacing, layer hierarchy, resizing constraints. From there, it generates HTML/CSS that follows that structure directly instead of guessing pixel by pixel:

• Auto Layout properties (direction, gap, padding, alignment) map onto equivalent CSS Flexbox rules.
• The layout's resizing constraints translate into responsive breakpoints, instead of a single fixed-width page.
• Output favors meaningful HTML elements over deeply nested, unlabeled <div>s.

Building from structure rather than guessing from an image is why the result preserves the original design's hierarchy and spacing, instead of something that merely "looks right" while the underlying CSS is completely different.

Choosing the right AI model for the job

MarkupGen lets you choose between AI models wherever the choice matters — for example, when regenerating code after an evaluation. There are two tiers: a fast, cost-efficient model suited to small or medium designs, and a higher-quality model suited to larger designs with more complex components. For accounts that don't manually pick a model, the system automatically selects the appropriate one based on the current plan, balancing speed and cost without requiring configuration.

Automated output quality scoring

Once code is generated, AI compares a screenshot of the live preview against the original design and scores how closely they match on a 1–10 scale. That's not an abstract number — it comes with a detailed evaluation of what matches and what's off, so you know right away whether an export is production-ready or still needs work. Once the score clears a near-perfect threshold, the system treats there as nothing meaningful left for AI to fix, and hides unnecessary regeneration suggestions.

The feedback loop: analyze and regenerate

If an export falls short, MarkupGen doesn't just hand you a number and stop. The evaluation view compares the original design and the live preview side by side, with zoom for inspecting detail, generates a specific improvement suggestion, and lets you edit that suggestion before hitting "Regenerate with this change" — so the AI rebuilds the code around the feedback you actually want, instead of starting over at random.

Why this matters

All three layers point at the same goal: removing the mechanical translation from design to code so a developer's effort goes toward what actually needs human judgment — interaction details, edge cases, and integrating the result into a larger codebase. If you want to see all three layers working on your own design, you can try it for free.