AI Image Generators Still Struggle With Text. Here’s a Practical Workflow That Helps

Susan Nielson — Sun, 03 May 2026 13:56:57 +0000

AI image generators are great at mood, lighting, composition, and visual style.

But the moment you ask for a poster, a product mockup, a blog cover, or an Open Graph image with actual text on it, things can get messy fast.

You have probably seen it:

letters that look almost correct but are not real words
product labels with random extra characters
UI mockups where the layout looks good but the copy is nonsense
beautiful social preview images that still need manual cleanup in Figma or Photoshop

For developers, this is especially frustrating because many of our visual assets are text-heavy. Blog covers, documentation headers, app screenshots, landing page hero images, launch graphics, and social cards all depend on readable words.

After testing a few workflows, I have found that the best results usually come from treating text generation as a design constraint, not as an afterthought.

Here is the workflow I use.

1. Decide what text must be real

Before writing the prompt, separate the text into two groups.

Must be exact:

product name
headline
call-to-action
short label
version number
domain name

Can be visual filler:

tiny background text
fake dashboard rows
decorative notes
blurred documents
placeholder UI content

This matters because asking an image model to render too much exact copy increases failure risk.

A better prompt is not:

Create a SaaS dashboard with lots of analytics, menus, buttons, labels, reports, pricing information, and a hero headline.

A better prompt is:

Create a clean SaaS dashboard hero image. The only exact visible text should be: “Deploy Faster”. Other UI text should be abstract, blurred, or represented as simple blocks.

The model now knows what to protect.

2. Keep visible text short

AI image models handle short text much better than long paragraphs.

Good candidates:

“Ship Faster”
“AI Design Kit”
“Build in Public”
“New Dashboard”
“GPT Image Workflow”

Riskier candidates:

long subtitles
full taglines
multi-line pricing cards
detailed interface copy
legal or compliance text

If you need a long title, consider generating the image without the title and adding it later in CSS, Figma, Canva, or your publishing tool.

For example, for a blog post cover, I usually ask the model to generate the visual metaphor and leave the final headline as a separate layer.

3. Give text layout instructions explicitly

Do not just include the words. Describe where and how they should appear.

Useful details include:

text position: centered, top-left, on a label, on a screen
text style: bold sans-serif, engraved, printed, handwritten
background contrast: dark text on white card, white text on dark poster
spacing: generous padding, large readable letters
number of words: only one line, no extra text

Example prompt:

A modern blog cover image for a developer article about AI image generation. Dark gradient background, subtle abstract pixels, one centered white card. On the card, large bold sans-serif text: “Readable AI Text”. No other visible words. High contrast, clean layout, minimal design.

The phrase “No other visible words” is surprisingly important. Without it, models often invent decorative text everywhere.

4. Generate the image in two passes

For text-heavy visuals, I rarely try to get everything in one pass.

A more reliable flow:

Generate the base visual with simple or no text.
Pick the best composition.
Ask for one focused edit that adds or fixes the exact text.

This works better because the second pass has a smaller job.

Instead of asking for a complete product launch poster in one prompt, you can first generate:

A premium 3D product launch visual, black background, glass device mockup, blue glow, no visible text.

Then edit it with:

Add one large headline at the top: “Launch Week”. Keep all other areas text-free.

This is slower, but it usually saves time compared with fixing broken letters manually.

5. Use a model or tool that is optimized for text rendering

Not all image models are equally good at text.

If the image only needs mood and illustration, most modern tools are fine. But if the final asset includes readable text, labels, posters, packaging, or UI copy, text accuracy becomes the main feature.

For recent tests, I used a GPT Image 2 generator because it is focused on text rendering, image editing, upscaling, and watermark-free outputs. The important part is not the specific tool choice, but choosing one that treats text as a first-class use case instead of a lucky accident.

Whatever tool you use, test it with your real use case:

your product name
your language
your brand colors
your typical image size
your export format

A model that works well for English poster text may still struggle with Chinese, Japanese, Arabic, or mixed-language layouts.

6. Avoid “almost readable” text in production assets

This is the quality bar I use:

If a user can read the text, it must be correct.

If the text is not meant to be read, it should be clearly abstract, blurred, tiny, or decorative.

The danger zone is “almost readable” text. It makes an otherwise polished image feel cheap because the viewer notices something is wrong even if they do not stop to analyze it.

For production assets, I check:

spelling
letter shapes
extra symbols
repeated words
punctuation
alignment
brand name accuracy
mobile readability

This is especially important for Open Graph images because they are often viewed at small sizes inside feeds, chat previews, and search results.

7. Use this prompt structure

Here is a reusable structure:

Create [asset type] for [audience/use case].

Visual style:
[style, mood, colors, lighting, composition]

Exact text:
Only include this text: “[TEXT]”

Text placement:
[position, size, font style, contrast]

Constraints:
No other visible words. No random letters. No misspellings. Keep the design clean and readable at small sizes.

Example:

Create a blog cover image for a developer article about AI-generated UI assets.

Visual style:
Minimal dark interface, floating design components, subtle blue and purple gradients, modern SaaS aesthetic.

Exact text:
Only include this text: “AI UI Assets”

Text placement:
Large bold sans-serif text centered on a bright white card, high contrast, generous spacing.

Constraints:
No other visible words. No random letters. No misspellings. Keep the design clean and readable at small sizes.

8. Know when to stop using the image model

Sometimes the best workflow is hybrid.

Use AI for:

background scenes
visual metaphors
product context
illustration style
lighting and composition

Use design tools for:

final headlines
precise logos
legal text
dense UI copy
responsive layout variants

This is not a failure of AI. It is just good production discipline.

For developer content, a hybrid approach is often the fastest path: generate the visual, then overlay the exact headline in HTML, SVG, Figma, or your blog engine.

Final thoughts

AI image generation is getting much better, but text is still the part that separates a fun experiment from a usable production asset.

The biggest improvement does not come from a magic prompt. It comes from designing the image around text constraints:

make fewer words exact
keep those words short
specify placement and contrast
remove accidental text
use focused edits
verify the final image like you would verify UI copy

If you treat text as part of the system design, not just decoration, AI-generated images become much more useful for real developer workflows.

GPT Image 2 vs DALL-E 3: Which AI Image Generator Actually Renders Text Correctly?

Susan Nielson — Sat, 25 Apr 2026 15:25:31 +0000

If you have ever tried to generate a poster, a product label, or a banner with an AI image tool, you already know the problem: the text comes out garbled, misspelled, or completely made up.

This is the single biggest practical limitation of AI image generators for commercial work.

The Text Rendering Problem

Most AI image generators treat text as a visual texture rather than semantic content. They learn to approximate the look of text rather than generate specific, accurate characters.

The result: ask for a poster that says "Summer Sale 50% Off" and you get something that looks like text at a glance but falls apart under inspection.

How Each Model Performs

DALL-E 3

Text accuracy: ~70-80% for short phrases. Breaks down on longer sentences, numbers, and non-Latin scripts.

Midjourney v6

Text accuracy: ~40-50%. Still unreliable for anything you would put in front of a client.

FLUX.1 Pro

Reaches ~85% accuracy for Latin scripts. Non-Latin scripts remain weak.

GPT Image 2

Text accuracy: 99%+ for Latin scripts, strong multilingual support including Chinese, Japanese, Korean, Arabic. This comes from training the model to understand text as semantic content, not just pixels.

Real-World Test

I ran the same prompt through all four models:

A Mexican restaurant menu with the heading Tacos al Pastor $8.50 and a short description below

DALL-E 3: Heading mostly correct, price rendered incorrectly
Midjourney: Decorative-looking text, not actually readable
FLUX: Best open-source option, but still mangled the price
GPT Image 2: Exact text, correct price, readable at all sizes

When to Use GPT Image 2

GPT Image 2 is the right choice when:

Your output needs readable, accurate text (menus, posters, banners, product labels)
You need consistent colors for brand assets
You are working with Chinese, Japanese, Korean, or Arabic text
The image needs to be commercially usable without manual touch-up

For pure creative work with no text requirements, Midjourney is still competitive.

Try It

You can test GPT Image 2 at gptimager.com. New accounts get free credits, no credit card required.

Have you run your own text-rendering tests across models? What results did you get?

DEV Community: Susan Nielson