Writing Effective Image Generation Prompts

A strong image prompt reads like a description of a finished picture. A useful order is subject first (who or what, doing what), then medium (photograph, oil painting, 3D render, pencil sketch), style or era (art nouveau, 1970s film still), lighting (soft diffused, rim light, neon), composition and camera (close-up, wide shot, low angle, 35mm lens, shallow depth of field), color palette, and finally aspect ratio. Specific nouns beat adjectives: "a weathered fisherman mending nets" gives the model more to work with than "an interesting old man." How you phrase this depends on the model. Older Stable Diffusion models use a CLIP text encoder and respond well to comma-separated keywords, and they only read a limited number of tokens. Newer systems such as DALL-E 3, Imagen and FLUX use stronger language encoders and handle full sentences, spatial relationships and text in the image far better. In ChatGPT, DALL-E 3 prompts are rewritten by the chat model before generation, so what you type is not exactly what the image model receives. Prompting images differs from prompting chat models. An image model is not reasoning about your request; it associates words with visual features. That explains the most common mistake: writing "a beach with no people" can add people, because the word "people" is present. Use a negative prompt field or Midjourney's --no parameter instead. Polite instructions, explanations of purpose and long backstories usually add noise. Other misconceptions: quality tags like "masterpiece, 8k, best quality" helped some older community models but do little on many modern ones, and longer prompts are not automatically better, since extra terms compete for influence. The reliable method is iteration: fix the seed, change one element at a time, and keep notes on which words produced which effects.