Negative Prompt Examples
Improve AI Image Quality Faster

Many AI image problems are not caused by weak positive prompts. They happen because the model was never clearly told what to avoid. A strong negative prompt helps remove clutter, anatomy errors, warped text, duplicate objects, and cheap-looking detail.

This page stays practical. Instead of explaining the concept in abstract terms, it gives you usable negative prompt examples you can copy, trim, and adapt for portraits, product images, anime art, and text-heavy graphics.

What a negative prompt does

A negative prompt tells the model what should not appear in the result. It is especially useful when you want to reduce anatomy mistakes, messy backgrounds, uncontrolled reflections, misspelled text, low detail, or a generic low-end visual feel.

A general-purpose negative prompt template

If you want a starting point, use this and then remove anything that does not match your image type:

low quality, blurry, noisy, distorted anatomy, extra fingers, extra limbs, duplicate elements, bad composition, messy background, warped text, oversaturated colors, flat lighting, artifacts

Example 1: Portrait negative prompt

Portraits fail most often around hands, eyes, teeth, facial balance, and background cleanup. Do not try to block every possible problem. Focus on the ones that actually ruin the image first.

Positive Prompt

cinematic portrait of a young woman by a rainy window, soft side light, shallow depth of field, realistic skin texture, elegant neutral styling, premium editorial photography

Negative Prompt

extra fingers, malformed hands, asymmetrical eyes, waxy skin, plastic texture, unnatural teeth, duplicate features, blurry face, messy background, flat lighting, low detail

Copy PositiveCopy NegativeTry on Niji Journey

Example 2: Product photo negative prompt

Product imagery breaks fast when it looks artificial. Prioritize material quality, logo accuracy, reflection control, edge clarity, and scene cleanliness.

Positive Prompt

premium hero product photography of matte black wireless earbuds case on reflective acrylic, controlled rim light, dark luxury background, crisp edges, commercial advertising look

Negative Prompt

cheap plastic look, warped logo, incorrect label text, muddy reflections, cluttered props, blown highlights, weak shadows, oversaturated color, blurry edges, low resolution

Copy PositiveCopy NegativeTry on Niji Journey

Example 3: Anime and illustration negative prompt

Anime images often need cleanup around extra limbs, costume inconsistency, dirty line quality, and noisy backgrounds.

extra arms, extra legs, fused fingers, deformed face, bad hands, messy lineart, inconsistent costume details, cluttered background, muddy colors, low contrast, off-model character design

Example 4: Poster, label, and packaging negative prompt

As soon as text matters, you need to explicitly fight gibberish, stretched letters, and broken layout hierarchy.

misspelled text, gibberish letters, warped typography, inconsistent font shapes, broken layout, overlapping text, stretched label, unreadable headline, cluttered composition

How to write stronger negative prompts

• Start with 5 to 10 high-impact exclusions instead of a massive list
• Group by problem type instead of repeating synonyms
• For portraits, focus on anatomy and skin; for products, focus on material, label, and reflections; for posters, focus on text and layout
• If the result becomes too constrained, cut your negative prompt list in half and test again

How this changes by model

Not every model responds to negative prompts in the same way. Stable Diffusion usually benefits the most from explicit negative lists. Midjourney tends to work better with shorter and more selective exclusions. GPT Image style models often respond better when you phrase the exclusion naturally and tie it to the outcome you want.

Final takeaway

A negative prompt is not effective because it is long. It is effective because it targets the exact failure mode in the current output. If the image has bad hands, attack hands. If the label is warped, attack typography. That focused approach usually improves quality much faster than adding more positive adjectives.