Beyond 'Make It Clean' (Part 2): How to Turn an Image into a Website

In Part 1 of this series, we established that visual-first planning is key to preventing AI agents from generating generic, average-looking user interfaces. In this installment, inspired by Leon Lin's image-to-code methodologies, we explore our team's hands-on pipeline for translating high-fidelity section mockups into functional, pixel-perfect frontend code using Codex.

Section 1: The Design Reference Foundation

The transition from idea to code starts by generating visual references for your website section-by-section. In our agency build, we began by loading a frontend design skill into our image generator. We requested an editorial, minimalist aesthetic with an orange palette, generating a separate, detailed visual reference for each page block:

Use visual guidelines: generate design comps for an AI agency website. Build distinct visual concepts section-by-section. Style: minimalist editorial, high typographic contrast, orange accents.

Generating individual sections separately (rather than a single full-page comp) ensures that the AI model has enough resolution to render rich details, clean borders, and appropriate margins.

Figure 1: Segmented visual comps ensure every block gets the detail and layout width it needs.

Section 2: Isolating Sub-Assets for Code Integration

A visual reference comp often contains complex background graphics, 3D illustrations, or custom mascots. The AI agent cannot code these graphics from scratch. We isolate these assets by prompting the image generator to output them individually as standalone elements:

Identify the primary graphic icons and illustrations inside the hero section mockup. Extract and output them as individual, high-resolution visual assets while preserving their exact style.

Figure 2: Isolating sub-assets individually provides standalone files ready for layout placement.

Section 3: Background Removal and Asset Optimization

Because current image generators usually output assets with solid backgrounds, we apply a local background removal pass. Masking out the background ensures the graphic element has a transparent alpha channel. We convert the cleaned assets to WebP formats to maximize page loading speed and keep our asset pipeline clean.

Figure 3: Clean edge masking lets transparent graphics scale cleanly against dark or light layouts.

Section 4: Incremental Code Assembly

Once our assets are optimized and our visual comps are ready, we hand them over to Codex to build our Next.js page. We instruct the AI agent to focus strictly on structural replication, preventing it from inventing placeholder assets:

Let's assemble a Next.js section. I will provide the optimized transparent assets and the reference layout comp. Replicate the layout, alignment, and spacing exactly, referring to the assets placed in the local folder.

We assemble the layout section-by-section. We feed the agent the assets, start the local development server, and verify the resulting layout in real time.

Section 5: Closing the Spacing Gap via Screenshot QA

The initial code draft gets you 80% of the way there, but spacing, letter-spacing, and line-heights often need adjustment. To close the remaining gap, we take a screenshot of our live local server build, overlay it with the design reference comp, and feed it back to Codex with exact spatial correction notes.

Figure 4: Using screenshots of the live build to guide the AI agent through layout corrections.

Section 6: Adding Interactive Polish

With the static layout validated, we prompt Codex to add fine visual details. We add smooth scroll-triggered transitions and subtle hover animations. This final pass transforms a static graphic layout into a highly responsive, expensive-feeling web experience.

Moving beyond average AI layouts requires separating layout design from code execution. By treating design references as a visual blueprint, engineers can achieve custom, high-end layouts with AI agents.