A Repeatable Image Workflow for Small Teams
Set up shared prompts, reviews and records so a team produces images reliably
A taste of a lesson
A client asked how we made an image from last year and nobody knows. What should we have kept?
A short record per final image would have answered it in a minute: tool name, date, the full prompt, key settings and seed if your tool uses one, the references and where they came from, any edits made afterwards, and who approved it. Even if you cannot reproduce it exactly, because tools change, you can explain how it was made and show you had the rights to your references. A shared sheet with one row per final is enough. Which three of those fields would your team actually fill in under deadline pressure?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Map your team's current image process and its failure points
- Create a request template and a shared prompt library
- Set up naming, versioning and a per image record
- Run a review checklist covering quality, rights and disclosure
Lesson plan
- 1 Map the current process Draw how images are made today and mark where problems appear. Start
- 2 Requests and briefs Use a short request format that gives makers what they need. Start
- 3 The shared prompt library Build a small, owned library of templates that produce on brand results. Start
- 4 Naming, versions and records Make every final image findable and reproducible. Start
- 5 Review with a checklist Catch quality, rights and disclosure problems before images go out. Start
- 6 Keep it alive Maintain the workflow as tools and people change. Start
Try asking
About this tutor
For small teams, studios and departments where several people make AI images and the results are inconsistent, untracked or risky. This tutor helps you design a simple, tool neutral workflow: a request format, a shared prompt library, a naming and versioning scheme, a review step with a checklist, and a record of which tool, settings and references produced each final image. We also cover rights and data rules, who approves what, and how to keep the system light enough that people actually use it. Suitable for any level: beginners learn good habits early, experienced makers bring order to existing chaos.
Reviews
4.5
2 ratingsSample
- Ingrid S.Sample
We are four people and had chaos. The request template and naming pattern alone cut a lot of back and forth.
- Marcus O.Sample
Sensible and not over engineered. I would have liked an example spreadsheet layout, though I built one from its description.
About the teacher
Image generation taught as a visual craft: prompts, light, consistency and editing
9 tutors 466 lessons taught Sample
I teach people how to make images with AI that look intended rather than accidental. My background is in photography and retouching, so I approach image models the way I approach a shoot: decide what the picture is for, plan the subject, the framing and the light, then fix what goes wrong. I like short exercises where you write a...
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