AI Image Prompt Tips

The more specific your prompt, the more predictable and accurate the output. Compare 'a city at night' with 'a rain-soaked street in Tokyo at midnight, neon signs reflected in puddles, cinematic lighting, wide angle'. The second prompt constrains the model to a much narrower range of outputs, which is what you want when you have a clear vision.

Most generators interpret prompts as a weighted list of attributes. Separate distinct ideas with commas. Each clause adds weight to that concept. Place your most important attributes early in the prompt as many models weight earlier tokens more heavily.

Referencing known artistic styles, movements, or visual approaches gives the model precise style guidance. Terms like impressionist, brutalist, art deco, vaporwave, or low poly activate style knowledge baked into the model during training. You can combine multiple style references for hybrid results.

For photorealistic images, describe it as if you were a photographer setting up a shot. Lens type, focal length, aperture, and shooting conditions all help the model produce convincing photography-like results. This is more effective than simply writing 'photorealistic'.

Many generators support negative prompts where you list things you do not want in the image. This is especially useful for removing common artefacts like extra fingers, blurry areas, watermarks, text, or specific unwanted styles. Not all tools expose negative prompts in their interface; check the documentation for your chosen generator.

Start with a broad prompt, see what you get, then refine specific elements. Changing too many things at once makes it hard to know what caused an improvement. Fix one element per iteration. Most generators are fast enough that you can run 5 to 10 iterations in under a minute, so use this to your advantage.

Product photography prompts focus on setting, lighting, and angle. Portrait prompts focus on emotion, expression, and background separation. Landscape prompts focus on time of day, weather, and atmosphere. Building a library of prompt structures for your recurring use cases dramatically speeds up your workflow.