Negative prompts: what they do and don't do

"Just add a negative prompt to fix ugly hands." It's one of the most shared AI tips, but whether it works depends entirely on the model you're using. In some tools a negative prompt does exactly what you hope, in others it does nothing or even backfires. Here's what a negative prompt really does, when it helps and what to do in tools that don't have one.

What a negative prompt is

A negative prompt is a short list of what you don't want to see in your image. In classic image generators it's a separate field next to your regular prompt.

Technically it works through classifier-free guidance. At each step the model makes two predictions: one based on your positive prompt and one based on your negative one. It then steers the image away from the negative and toward the positive. That steering only works if your tool uses a guidance setting above 1. Without it, there's no mechanism for the negative prompt to do anything at all.

What they're good for

If your model supports negative prompts, they're handy for problems that keep coming back:

• Quality: terms like blurry, lowres, jpeg artifacts and noise push back blurry or grainy results.
• Anatomy: extra fingers, bad hands and poorly drawn face lower the odds of botched hands and faces.
• Style: keeping a cartoon look out of a photorealistic image.
• Clutter: watermark, text and logo keep unwanted overlays out.

Note the word odds. A negative prompt lowers the likelihood that something appears, it guarantees nothing. How strong the effect is varies by model and by phrasing.

The pitfall: the model thinks in concepts

The biggest mistake is writing a negative prompt like a normal sentence. The model doesn't read language, it reads concepts. Write "no beautiful sunset" and the concept of a sunset is now in the model's attention anyway, which actually makes a sunset more likely.

So use bare keywords in your negative prompt, not sentences with "no" or "without". And don't overdo it: every term competes for the model's attention. List fifty of them and the model spreads its attention so thin that almost nothing has an effect anymore. A handful of targeted terms works better than an endless list.

Many modern models don't have one

Newer models often work without a separate negative field. Flux was deliberately not built for it: it uses a different technique than diffusion and runs at a guidance value of 1, so there's simply no way for a negative prompt to take effect.

Models you steer in plain language through a single text box, like Nano Banana and GPT Image, also have no separate negative field. Your exclusions have to sit inside your prompt itself. That's the case for the models behind AI Formule's tools: you type one description, not a separate list of bans.

What to do instead: phrase it positively

The most reliable approach in these models is to describe what you do want, as concretely as possible. Flip your exclusion into a positive requirement:

• Instead of "no extra fingers": "hands with five fingers, natural pose".
• Instead of "not blurry": "sharp focus, fine detail".
• Instead of "no busy background": "calm, even background".

If you really need to exclude something, weave it into your sentence briefly and clearly, for example "clean background, no text or watermark". Keep it to the few things that actually matter.

How to handle it in AI Formule

In the photo generator and the video generator you don't put exclusions in a separate field, but in your regular prompt, preferably phrased positively. Describe the scene, the light and the quality you want, and the model has less room to go wrong.

Not sure how to write that compactly? Let the prompt generator turn it into a fully worked prompt, including sharpness, lighting and composition.

And because you pay per render from freely topped-up credit, you can calmly try a few phrasings side by side without a failed attempt costing you a whole monthly fee.

Ready to test it? Create an account and compare for yourself what a sharp, positive prompt produces.