AI image generators default to generic output when given generic instruction, the same way a photographer with no brief defaults to safe, unremarkable choices. The fix isn't a secret trick. It's the same discipline that's always separated a real creative brief from a vague one: name the subject precisely, name the environment precisely, name the lighting and composition precisely, in that order.
What actually separates a flat prompt from a strong one
A prompt like "a photo of a coffee cup" produces something generic. A prompt specifying a close-up product photograph of a ceramic espresso cup on a white marble surface, shot with a 50mm lens, shallow depth of field, warm morning light from the left, in an editorial food photography style, produces something that looks like it belongs in a magazine. The subject didn't change. The level of instruction did.
This isn't a trick specific to one platform. Across image generation tools broadly, specificity around lighting, composition, and style dramatically affects output quality.
The rule, as a working structure
A reliable structure for image prompts strings together specific blocks of instruction, subject, environment, composition, lighting, style, camera, quality, and negatives, treated as modular components rather than one run-on sentence of adjectives. The subject deserves the most specificity, since vague subjects yield generic results, while a precise description of age, exact clothing, emotion, and pose gives the model something concrete to render.
This maps directly onto how a real photo director gives instruction on an actual shoot. Nobody hands a photographer the word "detailed" and expects a specific result. Saying what kind of detail is wanted, visible wood grain texture, individual eyelashes, an intricate lacework pattern, gives the model an actual target instead of a vague instruction to add more stuff.
Where this connects to a bigger principle
This is the same lesson as why ungoverned AI design output drifts generic in the first place: a system given no real constraints defaults to the path of least resistance. A prompt is a constraint. A vague prompt is a weak constraint, and the model fills the gap with whatever's statistically safest and least distinctive.
How to apply it
Order matters. Establish the subject first, with real specificity, before adding anything about style or mood.
Replace vague intensifiers with concrete targets. Instead of "detailed" or "high quality," name the actual visual element that should carry detail.
Borrow real photographic and compositional language deliberately. Lens length, depth of field, lighting direction, named compositional techniques like the rule of thirds, these are the same vocabulary a real photo director uses to get a specific result instead of a lucky one.
Treat the prompt as a brief, not a wish. Specific, named constraints produce specific, intentional results. Vague instruction produces the average of everything the model has seen, which is, definitionally, generic.