# Image Generation for Design: What AI Is Good For and What It Isn't > AI image generation works for moodboards, illustrations and concepts, not for exact screens. When to use it, how to use it, and what to check before publishing. *Tags: ai, image-generation, tools, design-workflow* --- > [!info] Quick Definition > **Image generation for design** is the use of AI models that create images from text or other images to explore styles, illustrations and visual concepts. It helps you think about the picture. It doesn't deliver the interface. ## What is an image generator, and what isn't it? An image generator produces a complete picture from a prompt, and sometimes from a reference image. The result is a bitmap: no layers, no components, no editable text. That is what separates it from design tools, where every element has a position, a name and properties you can change without redoing everything. That difference defines how you should use it. An AI-generated moodboard is a good starting point: it helps you decide whether the brand wants warm or cool light, paper or glass textures. An AI-generated screen, on the other hand, is a portrait of a screen, not a screen. If someone wants to move a button, you have to generate a new image or rebuild it by hand. ## Why does it matter to a designer? Because a generated image looks finished, and that deceives. Illegible text or a button that doesn't exist look just as polished as the parts that are right, and they slip through a quick review. The risk is concrete when the image represents your product. The App Store Review Guidelines (section 2.3.3) ask that screenshots show the app in use, not merely the title art, login page or splash screen. They allow text and image overlays on the screenshot, but the screenshot has to be of the app. An invented screen doesn't meet that requirement. A 2026 Screenhance article on mockup generators puts it bluntly: the model may invent controls, text, logos and hardware details, and you shouldn't depend on it to reproduce exact product UI or readable copy. It recommends compositing the real interface and setting the final text separately, and using AI to generate the hand, desk or lighting around a real screenshot. ## Which tasks is it good for, and which aren't? | Need | Reasonable tool | What to check | |---|---|---| | Explore style and mood | Image generator | That the directions differ from each other | | Supporting illustrations | Generator plus manual retouching | Hands, perspective, invented text | | Backgrounds and scenes for a real screenshot | Generator with the screenshot masked | That the screen isn't altered | | Editable screen or prototype | Design tool or UI generator | Layers, components and tokens | | Exact text (prices, legal) | Type set by hand | Never leave it to the model | According to Magic Patterns (a vendor of these tools), UI generators are trained on interface patterns and can pull components from your imported library. A general image generator like Midjourney, in contrast, "doesn't understand layout hierarchy, user flow, or component behavior," according to that same source. If your goal is a screen the team can build, start with [[design-tokens-for-agents|design tokens]] and real components, and keep the image for atmosphere. ## How do you use it step by step in a product workflow? 1. **Decide what the image is for.** Internal concept, published illustration or marketing background. The destination decides how much review it needs. 2. **Write a short style guide.** Palette, type of light, framing, materials and what you don't want to see. Save it next to the prompt; it's what lets you repeat the result another day. For writing good prompts, see [[prompt-engineering|prompt engineering for designers]]. 3. **Generate several directions, not one.** Ask for distinct variants and compare them side by side before refining. 4. **Pick and retouch.** Remove invented text, fix hands and perspective, and adjust the color to the brand palette. 5. **Put the exact parts on top.** Real screenshots, the official logo and final copy go on separate layers, not inside the generated image. 6. **Review the full series.** For a series (onboarding illustrations, for example), the problem isn't one good image but fifteen coherent ones. That topic has its own article: [[consistent-ai-illustrations|consistent AI illustrations]]. 7. **Check the tool's terms** before using the image in a product or a campaign. The image sets the tone; your team's [design system components](/en/wiki/concepts/design-system/) build the interface. ## What about licensing, rights and transparency? Three questions before you publish: - **What was the model trained on?** Each vendor explains it differently. Adobe, for example, says it trains Firefly on licensed content, such as Adobe Stock, and on public domain content where copyright has expired, and that it doesn't train on user content. Other vendors publish less. Read the policy of the tool you use. - **Can you claim rights over the output?** In the United States, the Copyright Office concluded in Part 2 of its AI report (January 29, 2025) that, with currently available technology, prompts alone don't provide enough human control to make you the author of the output. The parts with human contribution, such as modifications or the selection and arrangement of elements, can be protected case by case. Other countries have their own rules; if the image is a brand asset, check with legal. - **Do you have to say an AI made it?** Some tools add provenance metadata. Adobe, for example, is a founding member of the Content Authenticity Initiative and uses Content Credentials to show how content was created. When and how to tell users is covered in [[ai-disclosure|AI disclosure]]. ## When should you NOT use it? Avoid it when text must be exact (legal copy, prices, safety instructions), when the image represents a real product the customer will buy, or when the brand requires its own recognizable illustration. Also avoid it when you need a screen the team will build: there the image only adds a translation step. In those cases, an illustrator, real photography or the design tool is still the right choice. ## Example Illustrative example: a product team needs a cover image for a blog post about payments. They write a three-line style guide (soft light, the brand's green tones, no people), generate four scenes and pick the one with the cleanest composition. They retouch it to remove text the model invented on a credit card. On top they place the real screenshot of the payment screen, unchanged, and the headline in the brand typeface. They save the prompt and the guide in the project folder for the next cover. Nobody publishes a screen that doesn't exist. ## Common mistakes - Publishing a generated capture of the app, or one with text the model invented. - Asking an image generator to reproduce an exact design system. - Reviewing each image in a series separately instead of the set as a whole. - Not saving the prompt and the style guide, so the next session comes out different. - Forgetting licensing and training-data terms when the image goes into a product. - Assuming the generated image belongs exclusively to you without checking the terms or the applicable law. - Using the image as the final interface instead of as a visual reference. ## Sources - [Apple App Store Review Guidelines, 2.3.3 (Screenshots)](https://developer.apple.com/app-store/review/guidelines/) - [Screenhance: AI Mockup Generators in 2026: Where They Help and Where They Fail](https://www.screenhance.com/blog/ai-mockup-generators-2026) - [Magic Patterns: AI Mockup Generators](https://www.magicpatterns.com/blog/ai-mockup-generator) - [Adobe: The Adobe Firefly approach to generative AI](https://www.adobe.com/fireflyapproach) - [U.S. Copyright Office: Copyright and Artificial Intelligence, Part 2: Copyrightability (PDF)](https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-2-Copyrightability-Report.pdf) --- Source: https://www.fernandoux.com/en/wiki/ai/image-generation-for-design/