What is a negative prompt?

A negative prompt is the part of an AI image prompt where you list what should not appear: extra fingers, noise, messy background, watermarks, unwanted style, or accidental objects. It does not replace the main prompt; it narrows the output boundaries.

How do you write a negative prompt?

Describe the desired image first, then add 3-7 specific bans that match the scene risk. Do not copy universal lists. For portraits, mention hands, skin, and background; for product shots, mention shape, material, extra objects, and reflections.

How do you remove AI image artifacts?

Name the artifact and turn it into a direct constraint: not «bad quality», but «no extra fingers», «no warped logo», «no melted edges», or «no unreadable text». If the model does not support negative prompts well, move the same constraint into positive language.

Why can negative prompts make images worse?

A long or generic negative prompt pulls attention away from the main scene. Bans like «ugly, bad, worst quality» are not clear visual instructions. Short, specific constraints reduce the risk of losing composition.

Do you need a negative prompt for GPT Image 2?

GPT Image 2 often works better with natural positive constraints: «keep background clean», «preserve five fingers», «avoid text in the scene». If your interface has a separate negative field, keep it short and avoid old Stable Diffusion tag dumps.

Guide

Negative prompt: remove AI image artifacts

Vlad Voronezhtsev · May 27, 2026 · Updated: August 21, 2026 · 6 min read

Cover image for a negative prompt guide for AI image generation

A negative prompt is the constraint block in an AI image prompt: it tells the model what must not appear in the frame. It helps with artifacts, extra fingers, noisy backgrounds, unwanted styles, and accidental objects, but it works best beside a clear positive scene description, not as a random pile of banned words.

1.
Separate what to keep from what to remove
Write the positive prompt first: subject, setting, light, material, camera angle, and intended mood. Then add the negative prompt. If you start with bans, the model gets a map of what to avoid but no strong target. In Imagen 4 Ultra and Seedream 5, this is obvious on product imagery: «matte ceramic cup on a dark table, soft side light» is more stable than a long «no plastic, no glare, no blur» list without the actual scene.
Before
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negative prompt: ugly, bad, blurry, plastic, weird, deformed
```
After
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Positive: matte ceramic cup on a dark table, soft side light. Negative: no glossy plastic, no warped handle, no extra objects.
```
2.
Use soft blocks instead of junk lists
The query «how to remove AI image artifacts» is usually solved by a short list of exact risks, not by a hundred inherited tags. For portraits, those risks are hands, skin, teeth, background, and unwanted objects. For a logo, they are tiny text, pseudo-letters, complex shadows, and photo texture. In Midjourney 8.2, the official `--no` parameter tells the model what should not appear; use it for specific objects or traits and keep desired style control in the main prompt.
Before
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no ugly, no bad anatomy, no low quality, no worst quality, no jpeg artifacts, no mutation, no noise, no text, no logo
```
After
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no extra fingers, no melted jewelry, no text in background, no plastic skin
```
3.
Keep the negative prompt to 3-7 rules
The longer the banned list gets, the more likely the model is to fight itself. The practical rule is simple: keep only the mistakes that are plausible for this specific image. If you generate a product shot, there is no reason to ban extra fingers. If you generate a portrait, do not spend space on «no watermark» unless the scene has a real watermark risk. GPT Image 2 and Nano Banana Pro often respond better to positive constraints like «clean empty background, readable product silhouette» than to a bloated negative tail.
Before
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no hands, no people, no cars, no animals, no city, no text, no blur, no artifacts, no bad quality, no shadows, no reflections, no dust
```
After
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no extra objects, no warped edges, no unreadable text, no harsh reflections
```
4.
Test the model on the first render
Use the first render as a diagnostic, not as proof of a universal recipe. If a fashion image has glossy skin or a malformed hand, do not reach first for a vague `bad anatomy` tag. Test a precise patch: `natural skin texture, preserve five fingers on each visible hand; negative: no glossy plastic skin, no extra fingers, no fused fingers`. Compare it with the same seed or the closest available settings while changing only that block, then keep the constraint only if it helps your model.
Before
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Fashion portrait in a green coat, studio light, magazine look. Negative: bad anatomy.
```
After
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Fashion portrait in a green coat, studio light. Preserve five fingers on each visible hand. Negative: no glossy plastic skin, no extra fingers, no fused fingers.
```