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Build a prompt you can reuse
Start with the image’s purpose and subject, then describe what should be visible. Include the action, setting, composition, and details that matter to the final use. Concrete instructions—such as where the subject sits in frame, the direction of the light, material, color, or camera framing—are easier to evaluate than a broad mood word.
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A useful base prompt can be short. OpenAI Academy notes that “A good image prompt does not need to be long” in its Creating images with ChatGPT guidance. Add detail where it removes ambiguity, not just to make the prompt longer. For a complex scene, separate the prompt into labeled parts such as scene, subject, composition, details, and constraints so you can revise one part without losing the others.
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- Purpose and subject: what the image is for and what it depicts.
- Action and setting: what is happening and where.
- Composition: framing, placement, scale, gaze, and pose.
- Visible details: lighting, colors, materials, and any objects or interactions that must read clearly.
- Constraints: what must be present, excluded, or kept consistent.
For people interacting with objects, specify the pose and how hands and objects relate. If the image must contain exact wording, put the text in quotation marks, describe its location and typography, and inspect the output for spelling and legibility.
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Make edits specific—and protect what should not change
When editing an existing image, separate the requested change from the parts to preserve. Name the target precisely, then state the relevant invariants: for example, identity, layout, geometry, lighting, framing, or surrounding objects. Avoid instructions such as “make it better” when you can identify the change you actually want.
A reusable edit instruction is: Change only [requested element]. Keep [critical details] unchanged. Do not add [unwanted elements]. For API use, provide the source image with the edit prompt. In ChatGPT, describe the edit and use the selection tool if you need to target a particular area; see OpenAI’s Images in ChatGPT guide.
Repeat especially important preservation instructions in later turns. An edit can still alter details you meant to keep. If a region must remain pixel-identical, prompting alone is not a reliable way to guarantee that: OpenAI’s image prompting guidance recommends compositing the approved edit into the original image.
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Use ChatGPT controls for the output you need
In ChatGPT, create or edit an image by describing the desired result. Use the selection tool when the change should apply to a particular area, and specify an aspect ratio when the destination requires one. These are ChatGPT interface controls; they are distinct from the parameters available in an API request. Interface options may differ from API labels.
Choose API settings separately from prompt wording
For API workflows, the current OpenAI image prompting guide describes GPT Image 2.5 Flare as the speed-oriented option and GPT Image 2.5 Sunburst as the quality-oriented option. Use those as starting points to test against your actual task, not as a promise that one will always produce more consistent results.
| API setting | Options described in the current guide | How to use it in a comparison |
|---|---|---|
| Model | GPT Image 2.5 Flare (speed-oriented); GPT Image 2.5 Sunburst (quality-oriented) | Compare against the same prompt, reference images, and output requirements. |
| Quality | auto, low, medium, high, xhigh, max |
Hold the setting steady during an initial comparison; test alternatives one at a time. |
| Size | auto or a custom resolution within the documented constraints |
Keep dimensions consistent when comparing outputs, and check they fit the intended destination. |
| Background | auto, opaque, transparent |
Choose the background behavior the destination needs, then keep it fixed during a comparison. |
These are API guide details, not a guarantee that labels or availability will remain unchanged or match every ChatGPT interface. The Images API edit reference also lists GPT Image 2.5 variants among its edit-model options.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test consistency with a controlled comparison
- Define acceptance criteria. Decide what counts as a usable result: for example, correct subject and composition, readable required text, preserved identity, or a specific aspect ratio.
- Save a baseline. Use representative prompts and reference images, and record the prompt and API settings used.
- Hold inputs steady. For the first comparison, keep the prompt, references, dimensions, and a supported quality setting constant.
- Repeat requests. Inspect multiple outputs against the same criteria. Repeating requests reveals whether the workflow meets your needs; it does not establish a universal consistency rate.
- Change one variable at a time. If the results miss the target, adjust a prompt detail or a request setting—not both at once—then compare again.
- Check the trade-off. Once the result meets the use case, test lower quality settings if latency matters. Higher quality settings do not guarantee a better result for every prompt.
For an API workflow, include the practical operating measures in your comparison: typical and slow response times, retries, and the cost of accepted images. Confirm current pricing rather than assuming that the speed-oriented model costs less.
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Consistency is best measured against the requirements of the job, not by expecting repeated requests to produce identical pixels. Check whether each output follows the instructions and preserves the details you named. OpenAI’s guidance provides workflow and parameter recommendations, but no consistency percentage or benchmark that can be applied to every prompt.
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