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Every image begins with words — a prompt. When you type a prompt, you’re giving the model a set of instructions to build a picture from. The model looks for structure and meaning, then fills in the visual logic based on the patterns it has learned from millions of examples. The best way to think of prompting is like giving directions rather than issuing commands. The more clearly you describe what matters, like the subject, the mood, the setting, the style, the closer the output will feel to what you had in mind. Good prompting isn’t about tricking the system or guessing the “right” words. It’s about learning to speak a shared language with the model.

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Keep your language clear and simple

Good prompting isn’t complicated. Models understand plain language best, and understand direction in the same order that a person would understand a scene, like what’s happening, where it’s happening, and how it should look or feel. You don’t have to use special words or secret formulas. Clear language gives the model less to guess and more to work with. Overly complex sentences or poetic phrasing can confuse the structure. For example: A mountain lake at sunrise, surrounded by pine trees and soft mist.
This gives the model a clear visual hierarchy of the lake first, the light next, and then the mist. Compare that to: A misty morning scene with pine trees and a lake and mountains and sunlight.
This second one feels scattered, and the model will respond the same way. Word order reflects priority.