Creating AI Images: What Actually Works Today

“Generate me some image” has turned into a genuinely useful tool for real work – provided you know its limits as precisely as its strengths. Knowing both sides saves you the tenth disappointing generation, and the frustration that comes with it.

Key takeaways

• AI imagery is strong on concepts and moods (moodboards, illustrations, mock-ups), weak on exact facts and text within the image.
• A scene description written like a director’s brief reliably beats a list of adjectives as a prompting strategy.
• Consistency across a whole series of images remains difficult to achieve without dedicated reference tools.
• Usage rights over the output are generously handled by most major providers – labelling duties are nonetheless growing noticeably.
• Real people as subjects are legally risky, whether or not a tool technically allows it.

What AI imagery can reliably do today

Image generators are strongest where it’s about concepts and moods, not exact facts: a moodboard for a rebrand, an illustration for a blog post, a product mock-up for an internal presentation before the real photo shoot happens. Hands now look anatomically correct in most cases – a problem that gave away almost every image not long ago. Lighting, perspective and material rendering also often look convincing enough today for anything that doesn’t come under direct legal or technical scrutiny. For social media graphics, blog headers or early campaign drafts, the quality is often good enough now without needing a photo shoot at all.

The practical benefit shows up especially in early project phases: a marketing team that previously depended on stock photos or a small outside commission for a first campaign mock-up can now run through several visual directions within minutes, before budget is even released for the final production. This doesn’t replace professional photography – it moves it to the point where it’s actually needed: right at the end, once the direction is already settled.

Image types at a glance: what works, what doesn’t

Image type	How well does it work?	Reason
Moodboard, mood image	Very well	Concept matters, not exact facts
Product mock-up, illustration	Well	Lighting, perspective, material usually convincing
Logo or signage with text	Weak	Lettering often comes out distorted
Image series with the same character	Weak without a reference tool	Consistency across several images hard to control
Diagram with exact figures	Very weak	Numbers in the image are a matter of luck

The table follows a simple principle: the more concrete and checkable a detail has to be (a brand name, an exact figure, the same face across ten images), the less reliable the AI output becomes. The more room for interpretation a motif allows, the more reliable the result.

Where it fails regularly

Three weaknesses remain stubborn. Text in the image – logos, signage, packaging copy – often comes out distorted or unreadable; for lettering, manual touch-up is usually worth it. Consistency across a series: making the same character or product look exactly the same across several images rarely works without dedicated reference tools – a real driver of effort for campaigns with recurring motifs. Anyone who regularly needs series (for product descriptions with a consistent visual style, say) often already knows this problem from its text counterpart – more in our article on AI product copy at scale. And third: precise figures or facts within the image (a chart, a clock face) are luck, not a reliable output. Anyone wanting to play it safe generates the image without the critical details and adds them afterwards in a classic image-editing tool – the time saved from the AI base stays almost entirely intact.

The workflow that works

The biggest jump in quality rarely comes from a better model, but from a better description – once you’ve internalised the following six steps, you can apply them regardless of the tool currently in use.

1. Scene description instead of an adjective list: instead of “epic, breathtaking, high-resolution”, write a director’s brief – who or what is visible, in what setting, in what light, from what camera angle.
2. Use a reference image if you have one – your own photo, a sketch, or an earlier result often steers style and composition more reliably than yet more text.
3. Add negative instructions: explicitly name what should definitely not appear in the image – this cuts down typical failure modes like doubled fingers or distorted faces.
4. Iterate in variants, instead of hoping for the one perfect hit: generate several versions.
5. Take the best variant as the new starting point and sharpen it in a targeted way, instead of starting completely from scratch.
6. Touch up text and fine detail by hand, instead of waiting for a perfect AI output.

Rights and labelling

Anyone publishing or commercially using generated images should keep two things in mind: the usage rights of the provider in question (most grant them generously) and growing labelling duties for AI-generated content, in advertising or with deceptively realistic motifs, for instance – our article on the EU AI Act in practice covers the legal framework in detail. Details on usage rights – including the question of who actually owns the output – are covered in our article AI and copyright. (This is not legal advice.)

In practice, a simple checklist before publishing pays off: is the provider’s usage licence suited to the intended purpose (private, commercial, resale)? Are real people, brands or logos recognisable in the image that don’t belong there? And is it clear, wherever the audience expects it or the regulation requires it, that the image is AI-generated? These three questions barely take a minute in practice, but they prevent the most expensive corrections.

The bottom line

AI imagery is a genuinely useful tool today for concepts, moods and early drafts – not a replacement for photo production, logo design, or series that need exact consistency. Use a director’s brief instead of an adjective list, work with reference images, and touch up text details by hand, and you’ll get usable results within a few attempts.

The next sensible step: take a concrete project of your own (blog header, moodboard, social graphic) as a test case – that reveals what’s actually good faster than any tool leaderboard. Anyone who notices their own requirements regularly go beyond simple mood images – because text, brand consistency or a whole image series are needed, say – should plan from the start that part of the work will still be done by hand or with classic image editing. AI imagery shortens the path to a usable result; it rarely replaces it completely.