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To get a useful app or web mockup from GPT Image 2, describe a specific screen as if it belongs to an existing product: explain its purpose, layout, visual hierarchy, spacing, interface elements, style, and must-keep constraints. Review the first render against those requirements, then request focused changes one at a time. OpenAI’s example starts with a practical mobile interface for a local farmers market, not an abstract concept image.
How to prompt GPT Image 2 for a usable interface preview
Give the model a product brief, not just an aesthetic. OpenAI advises describing the product as if it already exists and focusing on layout, hierarchy, spacing, and real interface elements. The goal is a preview that reads like a usable, shipped interface rather than a design sketch. OpenAI’s image-generation guide includes a local farmers market mobile app as a concrete example.
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Translate that approach to the screen you need. For a market app, name the page’s purpose and specify a header, a vendor list with photos and categories, market specials, location and hours, restrained accent colors, and whether a phone frame helps communicate the layout. For a web page, describe its audience, key task, content sections, and primary action in their intended order.
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Adapt this pattern to your product; it is a practical synthesis of OpenAI’s guidance, not a guaranteed formula:
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Create a realistic [mobile app or web page] UI mockup for [product and audience]. Show [primary task] with [named components and their order]. Make [primary action] visually clear. Use [spacing, type, color, and visual style] and keep [must-preserve requirements] consistent. It should look like a practical, shipped interface rather than concept art. [Place it in a device frame only if that framing helps the review.]
OpenAI’s own example begins, “Create a realistic mobile app UI mockup for a local farmers market.” Use that as a model for specificity, then replace the product and components with your own.
Make constraints and edits precise
Explicitly state the composition, style, and elements that must appear. If you are editing an existing image, say what should change and what must stay as it is. For example, ask to change the button color while preserving the page layout, text, and imagery. Focused requests make it easier to tell whether the intended change worked or caused an unrelated regression.
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Review each render before using it
Treat the first image as a draft for evaluation, not as proof that every requirement was met. Compare the result with your prompt and check the parts most likely to matter in a mockup:
- Required text: Check whether labels, headings, and other specified wording are accurate and legible.
- Required components: Confirm the named controls, sections, and content are present and arranged as requested.
- References: If you supplied reference images, check that their important identities and details remain intact.
- Edit scope: After an edit, verify that the requested area changed and unrelated areas stayed consistent.
- Transparency: If you requested a transparent background, inspect the decoded image’s alpha channel rather than assuming the result is transparent.
These checks help distinguish a visually appealing image from a mockup that is actually reviewable against product requirements. For additional guidance on evaluating image outputs and iterations, consult OpenAI’s image-generation Cookbook example.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Set API options outside the prompt prose
When using the API, specify generation settings as parameters rather than relying on descriptive prompt wording to control them. The GPT Image 2 model identifier is gpt-image-2. The API reference documents quality settings of auto, low, medium, or high; size can be auto or a supported dimension; and output format can be PNG, JPEG, or WebP. Background is also a documented setting. Consult the current API reference for supported values and availability.
For GPT Image 2, OpenAI says image inputs are always processed at high fidelity and advises omitting input_fidelity. Do not add that setting based on instructions for another model or workflow.
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Requesting a transparent background
Transparent output for GPT Image 2 is documented as a preview capability. Request background="transparent" and choose PNG or WebP; then check the decoded output’s alpha channel to confirm transparency. The background setting alone is not a substitute for inspecting the returned image. See the image-generation guide for the documented options.
Compare iterations by what matters to your review
There is no basis here for naming a universally best UI-mockup model or design product. When comparing your own generations or settings, judge each result against the same representative prompt and requirements. Useful criteria include whether the specified text and components are faithful, whether reference details survive an edit, and whether revisions stay within the requested scope. Also account for the export dimensions and format, transparency needs, and the quality, latency, and cost trade-offs of the settings you test. OpenAI recommends evaluating quality, latency, and cost on representative inputs when changing prompts or models; see the Cookbook guidance.
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