An AI image workflow connects a placement brief to generation, review and revision. Define the crop and subject requirements first, compare a limited set of candidates, review each image in its intended layout and preserve the approved version. Measure usable deliverables and revision effort rather than counting generated images alone.
A generated image can look impressive in isolation and still be wrong for the job. It may leave no room for a headline, show the wrong number of products or lose its focal point when cropped for a phone screen. An effective AI image workflow begins with the destination and carries the original brief through generation, review and revision.
For small marketing teams, that workflow does not need an elaborate production system. A clear brief, a manageable set of candidates and a record of what changed can prevent repeated work. The goal is an approved asset that fits its actual placement, rather than a folder filled with visually interesting alternatives.
Workflow overview
- Define the destination, crop and visual constraints.
- Generate a small comparison set.
- Review candidates inside the intended layout.
- Revise the specific issue and preserve approved versions.
- Record accepted assets and production cost.
Write the placement into the brief
Begin with the format where the image will appear. A wide blog illustration and a vertical campaign image ask different things of the composition. Record the intended aspect ratio, crop behavior and any space that must remain clear for text. Describe what the image must communicate in one sentence.
Imagine an illustrative campaign for a reusable bottle. The brief might require one bottle on a picnic table, a visible carrying loop and empty space on the left for a headline. It might exclude people, logos and generated lettering. These are observable requirements that a reviewer can check without guessing what the author meant by a polished look.
Separate those requirements from preferences. A specific number of objects is usually a constraint; a warmer background may be a preference. This distinction helps the reviewer decide whether to reject a candidate or request a small change. It also reduces arguments about images that are attractive but unsuitable.
Change one important variable at a time
A first pass should test composition before fine detail. Generate a small set using the same brief, then compare object placement, background complexity and usable space. Resist changing the prompt, model and aspect ratio together. If the result improves, you will not know which change helped.
A practical trial could use three composition directions and two candidates for each. Those numbers are an example production budget, not a claim about the ideal number of generations. Decide the trial size before starting and increase it only when the review identifies a specific unanswered question.
Keep the exact prompt beside each candidate. A filename such as final-new-2 does not explain why an image exists. A short record containing the brief version, model identifier, generation settings and reviewer note makes the next revision much easier to reproduce or interpret.
Choose a tool around the handoff
A browser-based generator can work well when one person creates and exports images manually. An API becomes useful when an application must submit jobs, associate results with campaign records or repeat the same process across many assets. Choose according to the workflow you need to maintain.
OfoxAI includes image models alongside text and video models in its API platform. Teams considering an API workflow should check the chosen model’s input requirements, supported output sizes and response format before building the handoff. Those details determine what the application must save and what the reviewer will receive.
Do not infer that every image model supports the same editing controls. Some tasks require a reference image, others only a text prompt, and available parameters differ. Check the current model documentation for the specific operation. A common account or platform does not make model behavior identical.
Review the image at its real size
Evaluate a candidate inside a mockup of the intended page or post. The full-resolution preview can hide problems that become obvious in a small card. A subject near the edge may disappear in a crop. Background detail may compete with the headline. A subtle feature may be invisible on a phone.
Use a short acceptance checklist: correct subject, correct count, usable crop, sufficient text space and no unintended lettering. For product-related work, compare the image against approved product information and references. A generated illustration should not quietly change a feature that customers will interpret as real.
Assign one person to collect review comments. Conflicting instructions from several reviewers can create an endless loop of regeneration. Ask reviewers to describe the problem and its location: “the headline overlaps the handle” is actionable; “make it more premium” needs clarification before another generation.
Preserve useful revisions
Once a composition is approved, identify what must stay fixed. If the requested change concerns the background, preserve the accepted subject and placement where the selected tool permits it. Recreating everything from scratch can undo a decision the team has already made.
Keep the accepted original and the revised version as separate files. Record which one the reviewer approved and the reason for the revision. If a later export introduces an unwanted crop or compression problem, the team can return to the approved source without repeating the generation step.
For implementation details, consult the platform’s image generation and editing reference and verify the endpoint and model options before automating. Documentation should inform the request format; the creative brief should determine whether its output is usable.
Measure accepted assets, not just outputs
Track how many generated candidates became approved deliverables and how much review each required. An illustrative batch with 12 candidates and 3 accepted assets has a 25 percent acceptance rate. That figure explains the batch; it is not a benchmark for other teams or models.
Include generation charges, editing and reviewer time when comparing workflows. A lower price per output can be outweighed by repeated revisions. Conversely, a more expensive candidate may be economical if it meets the brief with less manual correction. Use your own records to make that decision.
Frequently asked questions
Do you need an API for a small campaign?
Only if automation or integration solves a real handoff problem. A disciplined manual process can be sufficient.
Should every rejected image trigger regeneration?
No. First identify whether the issue belongs in the brief, the crop, the editing step or the model choice.
What should the team archive?
Keep the approved asset, source brief, generation settings and final review note. Together they provide a useful starting point for the next campaign.
Author: Zoey, Growth at OfoxAI.
Reviewed on September 14, 2026.