DeepInfra raises $107M Series B to scale the inference cloud — read the announcement

Design Your Next Website With AI: A Prompting Guide for Ming-Image
Published on 2026.09.23 by Oguz Vuruskaner
Design Your Next Website With AI: A Prompting Guide for Ming-Image

Table of Contents

  1. Meet Ming-Image
  2. Vague Prompts Get You Vague Websites
  3. Write the Prompt Like a Design Brief
  4. Name the Aesthetic Explicitly
  5. Put the Real Words in the Prompt
  6. Lock the Seed, Then Change One Thing at a Time
  7. Design for the Square Canvas
  8. From Mockup to Editable Layers
  9. A Prompt Template You Can Copy
  10. Try It

Most website projects stall in the same place: the blank canvas. You can describe the vibe you want in a sentence — "clean, modern, a bold hero, one clear call to action" — but turning that sentence into a first screen someone can react to still means opening a design tool and pushing rectangles around for an hour. The gap between knowing the direction and having something to look at is where momentum goes to die.

Image models are supposed to close that gap, and mostly they don't. Ask a general text-to-image model for a website and you get a photo of a laptop on a desk, or a pretty picture with scrambled text where the buttons should be. Ming-Image is different because it was trained for exactly this job. Give it a real brief and it gives you back a real mockup — nav bar, hero, working typography, buttons that look like buttons.

Meet Ming-Image

Ming-Image-0.1-Design is an open-weight model from inclusionAI, tuned specifically for UI and interface design. At the time of writing it sits at the top of the Artificial Analysis UI/UX Design leaderboard, and it renders a full 1024×1024 mockup in about seven seconds. Its sibling, Ming-Image-0.1-Design-Layer, takes a finished mockup and splits it into separate layers you can edit.

Here's the thing that matters most, though: the difference between a throwaway image and a mockup you'd actually build from is almost entirely in the prompt. This post is about writing that prompt well. Every image below was generated with Ming-Image and nothing else.

Vague Prompts Get You Vague Websites

Start with the failure case. Here is what Ming-Image returns for the prompt a website:

A generic stock-photo render of a monitor on a desk with scrambled text

Technically a website. Useless as a mockup. Now the same model, same seed, given an actual brief:

A clean landing page for a product called Flow, with nav, a bold hero headline, a purple CTA button, and three feature cards

A real landing page — wordmark, nav, hero, CTA, feature row — from one detailed prompt. Same model, thirty seconds apart. The only variable that changed was how much the prompt told the model. That's the whole game.

Write the Prompt Like a Design Brief

The prompt behind that second image reads like something you'd hand a junior designer:

Landing page for a productivity app called Flow. Clean minimal design,
white background, generous whitespace. Top nav with the wordmark 'Flow'
on the left and links Product, Pricing, Docs on the right. Centered hero
with a bold headline 'Focus without the noise' and a subtle gray
subheading. A single purple call-to-action button labeled 'Start free'.
Below the hero, three feature cards with small icons, soft rounded
corners and soft shadows. Modern sans-serif typography.

Three habits make that prompt work, and they'll carry most of your results:

  • Name the page and the product. "Landing page for a productivity app called Flow" anchors the whole layout. The model designs for a purpose instead of averaging every website it has ever seen.
  • Describe the layout top to bottom. Nav, then hero, then CTA, then feature row. Ming-Image respects spatial order, so walking down the page in the prompt is the most reliable way to control composition.
  • Name the components. "Three feature cards with small icons, soft shadows" produces three feature cards with small icons and soft shadows. Ambiguity is the enemy; specificity is free.

Name the Aesthetic Explicitly

Layout tells the model what goes where. Style keywords tell it how the thing should feel — and this is where a few well-chosen words swing the result the hardest. The same instruction to "make a landing page" produces completely different, equally coherent designs depending on the aesthetic you name:

A dark glassmorphism SaaS landing page for a product called Signal with frosted cards and a floating dashboard preview

Keywords: "dark glassmorphism, deep navy, purple-to-blue gradient glow, frosted translucent cards."

A warm editorial photographer portfolio with a cream background, serif display headline, and a three-photo grid

Keywords: "warm editorial, cream background, large serif display headline, muted terracotta accent."

Reach for concrete, visual adjectives the model can actually draw — glassmorphism, frosted, gradient glow, serif display, editorial, brutalist, muted terracotta. Vague mood words like "premium" or "clean" help a little; naming a real visual style helps a lot.

Put the Real Words in the Prompt

General image models smear text into nonsense. Ming-Image renders it — so give it the actual copy you want on the page instead of leaving it to chance. Ask for specific labels and numbers and they come through:

A revenue analytics dashboard with a sidebar, three KPI cards showing exact numbers, and two charts

The prompt asked for KPI cards reading "MRR $48.2k", "Active users 12,904", and "Churn 1.8%" — and that's what rendered.

One caveat worth knowing: text fidelity is excellent for short strings — headlines, nav links, button labels, numbers, KPI cards — and still degrades on long body paragraphs, where you'll see believable-looking-but-fake glyphs if you look closely at the small print. So put your real headline, your real CTA, and your real data in the prompt, and drop long body copy in afterward in your design tool.

Lock the Seed, Then Change One Thing at a Time

Every hero shot above used seed: 7. The seed is the model's starting randomness — hold it fixed and the composition stays stable between runs; change it and you get a fresh take on the same brief.

That gives you a clean iteration loop. When a design is 90% there and you want the CTA green instead of purple, don't reroll blindly. Keep the seed, change the single clause (a purple call-to-action buttonan emerald call-to-action button), and regenerate. You get the same page with the one change, instead of starting over. Vary the seed only when you want genuinely different directions to choose from.

Design for the Square Canvas

Ming-Image renders on a 1:1 canvas — width and height should match (1024×1024 is the sweet spot). Treat that square as your above-the-fold hero viewport: compose the nav, headline, and primary CTA to land inside it, then export and extend the page vertically in your design tool. Asking for a tall, narrow full-page layout fights the model; designing the hero and building down from it works with it.

From Mockup to Editable Layers

A flat PNG is a starting point, not a deliverable. This is where Ming-Image-0.1-Design-Layer comes in: feed it a finished mockup and a layer count, and it lifts the salient elements onto their own layers so you can move them independently in a design tool.

The full Flow landing page mockup

Input: the finished mockup.

The same page with the call-to-action button separated onto its own layer

Output layer: the model has isolated the primary CTA from the rest of the composition.

Set expectations honestly here — on a minimal design it's best at peeling off a handful of key elements (a CTA, a hero block) rather than producing a full Photoshop-style stack. But even that is enough to skip the tedious first pass of tracing a mockup by hand before you can edit it.

A Prompt Template You Can Copy

Fill in the blanks and you're most of the way there:

[Page type] for a [product type] called [name].
[Aesthetic keywords], [background], [spacing].
Top nav with [logo/wordmark] and links [A, B, C].
Hero with the headline '[exact headline]' and [subheading].
A [color] call-to-action button labeled '[exact label]'.
Below the hero, [components: N cards / a chart / a photo grid].
[Typography]. [Accent color].

Try It

The fastest way in is the browser: paste a brief straight into the playground on the Ming-Image model page and iterate there.

Designing a website with AI was never about typing "make me a website" and hoping. It's about writing a brief precise enough that the model can only draw one thing — the thing you already had in your head. Get the prompt right and the blank canvas stops being where projects stall, and starts being where they start.

Related articles
Fine-Tuning vs RAG vs Prompting: 2026 GuideFine-Tuning vs RAG vs Prompting: 2026 Guide<p>When an AI system yields unreliable answers, the root cause could be an unclear system prompt, missing context, poor retrieval quality, or simply using the wrong base model. Teams end up spending weeks experimenting with prompt changes, retrieval-augmented generation (RAG), or fine-tuning to improve response quality. But before deciding which technique to adopt, it is [&hellip;]</p>
Are Chinese Open-Weight AI Models Still Safe to Use?Are Chinese Open-Weight AI Models Still Safe to Use?<p>Congress is asking American companies to explain their use of Chinese AI models. The House Homeland Security Committee and the Select Committee on the Chinese Communist Party opened a joint probe in April 2026, sending letters to Cursor and Airbnb. By July, DoorDash had received a similar inquiry. The State Department issued a formal warning [&hellip;]</p>
GLM-5.2 Pricing, Benchmarks, and Cost ComparisonGLM-5.2 Pricing, Benchmarks, and Cost Comparison<p>If you care about long-context reasoning but don&#8217;t want to lock yourself into a closed model, GLM 5.2 is worth attention for one simple reason: it pairs a 1M-token context window with open weights, MIT licensing, and a real provider market instead of a single take-it-or-leave-it endpoint. That makes it unusually relevant for teams doing [&hellip;]</p>