daisyUI v5.6 is now available!

Design website with AI

Design a website with AI by giving the model one component system, one theme layer, and clear UI primitives.

AI website design needs one visual system

AI can draft a website quickly, but each section often feels like it came from a different template. The hero has one style, pricing has another, and the form introduces a third color system.

That happens because "design a website" is too open. The model chooses layout, hierarchy, components, colors, spacing, and interaction states at the same time. A shared component and theme layer keeps those choices connected.

The hidden cost is not the first screen

The first version often looks acceptable. The maintenance cost shows up in the second, fifth, and tenth edit.

  • One button becomes five different button class strings.
  • A card uses different padding on each page.
  • Dark mode colors get copied by hand.
  • The model changes nearby styles while fixing one small detail.
  • Reviewing the diff takes longer than the prompt.

This is a system problem. The model needs stable names for repeated sections and controls before it can keep a full website coherent.

Give the model component names before it writes code

Semantic classes narrow the search space. Instead of asking AI to invent each website section from utilities, give it known targets:

<button class="btn btn-primary">Save changes</button>

The same idea works across a page:

<section class="grid gap-6 md:grid-cols-3">
  <div class="card bg-base-100 shadow-sm">
    <div class="card-body">
      <h2 class="card-title">Team usage</h2>
      <p>See seats, invites, and plan limits in one place.</p>
      <div class="card-actions justify-end">
        <button class="btn btn-primary">Manage</button>
      </div>
    </div>
  </div>
</section>

Now the prompt and the code share the same vocabulary. You can ask for "primary", "outline", "warning", "compact", or "ghost" instead of asking the model to rebuild color, border, spacing, hover, active, disabled, and focus styles.

Why daisyUI fits AI website design

daisyUI is a Tailwind CSS component library. Version 5 installs with @plugin "daisyui", includes 61 component families in this repo, ships 35 built-in themes, and can also be used from CDN with @tailwindcss/browser@4 for quick HTML prototypes. It adds CSS class names. It does not ship React, Vue, or Svelte components, so your framework keeps control of state and behavior.

That combination matters for generated UI. Tailwind CSS remains available for layout and one-off styling, while daisyUI handles repeated interface parts with names a model can reuse: btn, card, input, select, modal, navbar, menu, table, badge, alert, stat, and toast.

The model still has freedom. It can choose the layout, data, copy, and interaction wiring. The repetitive visual layer has a stable vocabulary.

A better prompt pattern

Use prompts that define the theme and component system before the page:

Build a settings page with daisyUI.
Use cards for sections, fieldsets for form groups, inputs for text fields,
selects for option lists, and btn-primary for the main action.
Use Tailwind utilities only for layout and spacing.

That prompt gives the model pieces it can assemble. It also gives you code that is easier to review because the important UI decisions are visible in class names.

When this approach pays off

Use semantic components when the site has more than one section, more than one page, or more than one round of edits. A website needs a visual system before it needs decorative variety.

Start with the daisyUI components, then keep the install guide and theme generator close while you prompt. The less the model has to invent, the more attention it can spend on the screen you asked for.

For full-page site generation, define the theme first. Tell the model which daisyUI theme or custom theme tokens to use, then ask for sections built from components such as hero, card, navbar, footer, and btn.

daisyUI is the most popular
component library for Tailwind CSS

Used by engineers at

Meta Research
Alibaba
Amazon
Adobe
Pepsico
Google Cloud