Social Rocket
AI images are getting technically better – and yet they often still look interchangeable. For social media teams, the problem shifts from pure image quality to art direction: if you only enter "make me a modern Instagram post", the model largely decides the subject, lighting, perspective, style and impact on its own.
Social Media Examiner describes a framework by AI designer Lauren deVane for this, with seven prompt building blocks: medium, subject and action, environment, composition, lighting, aesthetic and intended effect. It is not a technical standard, but a practical method for leaving fewer decisions to the model and making your own brand look more explicit. Read the original article at Social Media Examiner.
Why better image models don't automatically solve the brand problem
On 8 September 2026, OpenAI introduced ChatGPT Images 2.5. According to the company, the model improves, among other things, fidelity to reference images, precise edits and consistency across multiple editing steps. OpenAI also cites generation latency up to 50 percent lower compared with Images 2.0. OpenAI describes the changes here.
This is relevant for marketing teams, but it does not resolve the underlying creative question. A model can implement an instruction more accurately – it still needs to know which visual language is wanted. That is exactly where a reproducible prompt and brand workflow begins.
If you also work with other image tools, the Srocket article on Google Pics and AI image editing for social content gives an overview of another current approach.
The seven prompt building blocks for less generic AI images
1. Define the medium
Should the result look like product photography, editorial illustration, 3D scene, collage or graphic ad? "Image" is too open as an instruction. The medium sets the visual frame before details are added.
2. Describe subject and action
Don't just name what is visible, but also what is happening. A person "standing in an office" is less defined than a person who is comparing a draft on a tablet and pointing at a correction. Action creates more concrete scenes and prevents the typical AI stock-photo look.
3. Make the environment concrete
"Modern studio" leaves a lot of room for interpretation. Materials, surfaces, backgrounds, props or time of day make the scene clearer. What matters is not maximum length, but relevant precision.
4. Specify the composition
Close-up or wide shot? Portrait or landscape format? Plenty of free space for copy, or a subject filling the frame? Centred, asymmetrical or bird's-eye view? Especially for social ads and organic creatives, the later use should already be visible in the prompt.
5. Define the lighting
Lighting changes the effect of an image massively. "Soft window light from the left", "harsh midday sun" or "warmly lit interior scene" are more helpful to a model than vague terms like "nice lighting". OpenAI also recommends concrete details on lighting, framing and constraints in its own prompting guide. To the OpenAI Academy guide.
6. Translate the aesthetic into properties
Instead of only naming a known style or artist, the team should describe why a reference works: symmetrical composition, desaturated colours, strong grain, minimalist set, documentary camera or bold typography. This creates a distinct visual language rather than a mere style copy.
7. State the intended effect
Should a creative convey calm, urgency, premium quality, curiosity or product trust? The desired reaction belongs in the brief. For paid social in particular, this is more useful than a purely decorative "make it premium".
Reference images are part of the prompt
For brand-appropriate results, references are often more valuable than ever-longer text prompts. OpenAI explicitly recommends a small, manageable selection when uploading several images, and clear instructions on the role each image plays. When editing, it should also be stated explicitly what may change and what should stay the same.
For social teams, this could mean: a product photo, a style reference and an existing brand visual – combined with a clear instruction such as "keep product shape and logo unchanged; adapt only background, light and environment to the style reference".
This turns a single prompt into a small visual system. A similar idea lies behind repeatable AI workflows: the Srocket guide to Claude as an AI creative director is about treating brand rules and approvals as fixed parts of the process.
A practical workflow for social media teams
In practice, a lean process is usually enough.
Collect brand rules: document colours, typical perspectives, lighting, backgrounds, and permitted and unwanted style features in advance.
Build a prompt template: treat the seven building blocks as fields instead of starting from scratch every time.
Work with few references: upload only material relevant to the specific subject.
Test variations deliberately: don't rewrite the whole prompt each time, but change individual variables such as lighting, perspective or setting.
Check final creatives: review logos, text, product details, hands, resemblance of people and brand rules before publishing.
Document good prompts: working combinations become reusable templates for formats, campaigns and products.
This matters mainly because AI is being used widely in marketing, but organisational maturity does not automatically grow with it. A recent Srocket analysis shows the gap between high AI usage and significantly weaker success measurement.
What AI image generation does not replace
More control in the prompt replaces neither design judgement nor approvals. For real people, the use of their likeness should be clarified legally and organisationally. For portrait images, OpenAI recommends reference material with appropriate permission, and for design questions it advises independent, "ownable" variants rather than imitating specific brands or works.
Even with good models, final checks remain necessary. Text in images, logos, product packaging and recurring characters can vary across several assets. The most efficient workflow is therefore not "one prompt, one perfect image", but a short loop of template, generation, targeted correction and approval.
Conclusion: the difference increasingly lies in the creative brief
The better image models become, the less technical image quality alone is a competitive advantage. For brands, what increasingly matters is whether they can translate a recognisable visual language into clear instructions.
The seven-building-block framework is a sensible starting point for this: medium, subject, environment, composition, lighting, aesthetic and effect force teams to make the creative decisions themselves instead of leaving them to the model. That is what makes AI images less generic – and, at best, genuinely brand-ready.