How to fix AI-generated frontend code

You ask an AI to build a website. A few minutes later, you have a working page: a headline, some cards, a call to action. Everything is technically there, but it still feels like a first draft. The spacing is uneven. The copy could belong to any product. The page has no clear point of view.

That is a useful place to start. You have something concrete to inspect, and improving something concrete is much easier than debating an empty canvas. The next step is to replace vague requests for polish with a series of specific decisions.

1. Give the AI a direction it can follow

“Make it modern” leaves almost every design decision open. Start with the audience, the purpose of the page, and a reference you can actually show the assistant. A screenshot or an accessible page is more useful than assuming the model knows a brand’s current website.

Describe the qualities you want to learn from the reference: the size of the headline, the restraint in the color palette, the rhythm between sections. Keep your own content and identity. A reference should help you communicate decisions, not turn your product into a copy of somebody else’s.

2. Build a small, consistent foundation

Before asking for another section, inspect what already exists. If there is a button component, reuse it. If the project defines colors and spacing, work with those tokens. A visual redesign becomes harder to maintain when every component invents a slightly different shade or corner radius.

For a new project, create a modest structure that separates shared components, page layouts, and content. You do not need an elaborate architecture. You need a place for the next piece of work to belong.

src/
├── components/   # Shared navigation, buttons, article cards
├── pages/        # Page layouts
├── data/         # Article content and site information
├── hooks/        # Reusable behavior
└── index.css     # Type, color, spacing, and layout rules

• Choose one display typeface and a readable body face. Define sizes for headings, body copy, and labels.
• Set a small spacing scale. Reuse the same gaps for related elements across the site.
• Keep a clear distinction between a primary action, a secondary action, and a plain text link.
• Ask the assistant to refine one representative section first. Review it before extending the pattern.

3. Give every visual a job

Images help when they explain the product, establish a point of view, or give readers a useful visual landmark. They do not need to appear beside every sentence. Start with the most important image and decide what it should communicate before generating or sourcing it.

For an editorial site, a coherent family of article covers can make the journal feel intentional. For a product, a real screenshot may explain more than a decorative illustration. Keep the surrounding layout quiet enough that the image can do its job.

• Write an image brief with a subject, composition, materials, and a palette that belongs to the site.
• Use descriptive alt text for meaningful images. Use an empty alt attribute for purely decorative images.
• Export appropriately sized images and provide smaller variants for narrower screens.
• Reserve the image’s dimensions in the layout so content does not jump while it loads.

4. Review the experience, not just the screenshot

A polished desktop screenshot can hide a surprising number of problems. Resize the page. Navigate with a keyboard. Search for something that does not exist. Open a long article, follow a link, and use the browser’s back button. These ordinary actions expose the gaps that a visual pass misses.

Give feedback at the level of the problem you can observe. “The paragraph is difficult to scan at this width” is more useful than “the typography feels off.” Ask for one change, inspect the result, and keep the parts that already work.

The takeaway

A better frontend comes from a better sequence of decisions. Set the direction, establish the foundation, choose purposeful visuals, and test the experience. AI can help with every step, but the quality of the result still depends on what you ask it to notice.

You do not need to get everything right in the first prompt. You need to make the next draft more deliberate than the last one.