How-to auto create images for a blog post
*I like automation most when it admits that some images need a steering wheel, not just a button.*
I built a system to auto-generate hero images for blog posts. It works perfectly until it does not. The basic idea is tidy: take the essence of a post, turn it into an image prompt, combine that prompt with the relevant image style, and let the publishing workflow produce something usable. For many posts, that is exactly what happens. Then a Danish Christmas candle appears, or a diagram needs to make sense, and the fantasy of full automation quietly leaves the room.
The easy images really are easy
For some blogs, automatic image prompting is almost suspiciously effective. On Letshygge.com, for example, a post may need a generic illustration of a Christmas dinner. Not a documentary image. Not a portrait of real family, friends, or co-workers. Just the visual idea of a warm seasonal meal.
That kind of image is well suited to an essence-based workflow. The post says Christmas dinner; the prompt captures atmosphere, setting, objects, and mood; the style prompt keeps the output consistent with the site. No one needs the generated tablecloth to be historically perfect. No one is checking whether the gravy boat matches a specific household.
This is where automation earns its keep. It removes repetitive translation work. A post already contains enough semantic material to suggest a useful image, and the system can package that material faster than a human should have to. I do not think every blog image deserves artisanal prompt carpentry.
Some images are placeholders with taste.
The candle exposes the lie
The trouble begins when the image is still “simple” to a human but not simple to the model. A Danish Christmas candle is a good example. It is not enough to render a candle with numbers on it. The image needs the numbers from 1 to 24. All of them. Not a decorative suggestion of counting. Not a charming almost-calendar.
That required explicit prompt refinement: all numbers between 1 and 24 are required.
This is the sort of detail that makes automation look less like magic and more like a very fast intern with selective attention. The model can grasp the scene, but it may not respect the constraint unless the constraint is nailed to the table. The more culturally specific or structurally precise the image becomes, the less I trust a single extracted “essence” to carry the whole job.
The hard part is not generating an image; it is knowing when the generated image has misunderstood the assignment.
That is not a failure of the workflow. It is a boundary.
Diagrams are not decorations
The same problem becomes sharper on Jette-AI.com and zenk.dk, where a hero image may need to incorporate a diagram or some technical structure. A diagram is not just an aesthetic element. It has relationships, hierarchy, labels, arrows, flow, and sometimes an argument hidden in the layout.
For those images, the automated prompt is a starting point, not the final instruction. The prompt often needs additional refinement in ChatGPT or Claude before being copied back into the CMS workflow. Only then does it get combined with the image style context or seed image.
I like this hybrid pattern. The system does the boring extraction and formatting. The human-directed refinement handles the meaning-sensitive part. Then the CMS workflow takes over again. The process is not fully automatic, but it is still faster and more consistent than starting from a blank prompt every time.
The important part is not pretending otherwise. Complex images need iteration. Sometimes multiple versions. Sometimes a prompt that becomes more like a tiny specification than a creative request.
I think this is the honest future of AI-assisted publishing: automation for momentum, refinement for judgment. A generic Christmas dinner can glide through the machine. A numbered candle and a meaningful diagram deserve a hand on the wheel. Calling that a limitation misses the point; it is the part of the workflow that keeps the image connected to the idea.