The problem is not the image, it is the series
Looked at one at a time, AI-made post images are good. Side by side on a profile, the trouble shows: each one has slightly different colour, different light, different style. A follower cannot put a finger on what bothers them, but it does not add up to a brand.
That is not the model's fault. The model starts from nothing on every generation, and the smallest differences in the prompt take it somewhere else. So the answer is not to describe it better, it is to take the decision out of the generation: whatever belongs to the identity is fixed, and only the content changes from post to post.
The four layers of the template system
A system that works is built from four layers independent of each other — and the whole point is which layer holds what fixed.
- Brand layer (fixed). Colours, typeface, frame, logo position, margins. This is not generated, it is a template in an editor. It never changes.
- Style layer (fixed). A single sentence, always the same, describing the visual world you work in: photo or illustration, what light, what colour temperature. It appears in the prompt unchanged.
- Content layer (changes). Whatever the given post is about. This is the only part of the prompt you rewrite every week.
- Text layer (fixed structure, changing content). The position, size and typeface of the caption are fixed; only the words swap out. Important: the text is not generated into the image, it goes on in the template.
The brand frame
This layer decides whether the series holds together, and it is not an AI job. Practically every graphics tool has a brand kit feature where you put the colours, the typefaces and the logo. Worth knowing: in most places this feature depends on your plan — on the free tier you can typically keep one such kit, on the paid tier a handful, and on business plans a hundred or more. If you run several brands, that setting decides which plan you need.
As for locking down the visual style, there are two routes. Either you give a style reference image with every generation — the quick way — or you use your own trained style model. The latter exists today too, but at the large providers it is tied to a business plan, so as a one-person operation the reference-image route is the workable one. On the differences between reference types, character and product consistency covers the ground, because the same mechanism drives both.
The weekly workflow
This is the order that actually produces thirty images in one sitting.
1. Fifteen minutes: the month's topics in a list. Topics only, no image ideas.
2. One style sentence you append to every prompt. Example:
"documentary photograph, natural colours, soft side light,
slight grain, cool shadows"
3. One sentence on the content per topic. This is the variable part.
4. Generate in one batch, item by item. Do not pick as you go: collect
the raw images, and only look through them afterwards.
5. Selection: the best one per topic. Expect every third or fourth
to be usable — that is the normal ratio.
6. Template: the chosen images go into the brand frame, and that is
where the caption and the logo go on.
Labelling on the platforms
Social platforms today do two different things, and it is worth knowing which one means what for you. One is that they detect it themselves, from the image metadata or from a hidden signal, and put a badge on it. The other is that the uploader ticks a box themselves.
Practice differs from platform to platform: some have both automatic detection and self-declaration, some put a hidden signal on uploaded video themselves, and some currently have no mandatory self-declaration at all. Since this area moves fast, the reliable approach is to leave the provenance marking in the file and tick the box wherever there is one. On what these signals survive and what they do not, the mechanics of provenance goes into the detail.
What we know about whether labelling hurts
That is a fair business question, and there is research on it. The short answer: it is not the AI use that moves trust, it is the label — that is, the fact that the viewer knows. And the effect does not run one way: it depends strongly on the category.
The practical conclusion is not to hide it. For one thing, labelling is mandatory in several places; for another, being caught costs more than the label. What does matter: wherever authenticity is part of the product — a customer testimonial, the actual team, a real location — do not use an AI image. Where the image is an illustration, the price of the label is negligible.
Common misconceptions
- "If I use the same prompt for every image, it will be consistent." It will not: the same prompt gives a different image on every run. Consistency comes from the fixed template, not from the prompt.
- "Let the model put the caption on too, it looks better that way." Across a series that is the fastest route to messy typography, which text rendered into an image unpacks separately. Text laid on in the template is always more readable.
- "I will have the logo generated into it as well." Never. The logo goes on from a vector file, in the same spot on every image. On why that is not an AI job, logos and brand assets has the argument.
- "One good prompt is enough, then it runs itself." Building the prompt is one half of the work — and the layers of an image prompt takes that apart — while the other half is the template and the selection.
What to take away
The secret of producing a series is not in the prompt, it is in what you fix. One fixed brand template, one fixed style sentence, and a single variable sentence per post: that is what gives you thirty images that look like one brand side by side. Generation is the fastest step in this system — the rest is housekeeping.