A negative prompt is a text input used in image and video generation systems to specify concepts, styles, or visual artifacts that a model should avoid producing, used alongside, and separately from, the main descriptive Prompt. Where an ordinary prompt describes what an output should contain, a negative prompt describes what it should not.
How it works
Most negative prompt implementations rely on a technique called classifier-free guidance, in which a Diffusion model generates an image by comparing predictions made with and without conditioning on the prompt text, then steering the output within the model's Latent space toward the described content and away from undesired content. When a negative prompt is supplied, the model performs an analogous comparison that pushes the output away from the concepts named in the negative prompt as well, rather than only away from an unconditioned baseline. This makes negative prompting most naturally suited to diffusion-based systems, and it is less commonly exposed in other generation approaches.
Common uses
Negative prompts are typically used to suppress recurring failure modes of image generators, such as distorted hands and faces, extra limbs, blurry or low-resolution output, watermarks, signatures, and unwanted text artifacts. They are also used stylistically, for example excluding certain color palettes, art styles, or compositional elements that a base Text-to-image generation prompt tends to produce by default. Some communities maintain shared libraries of standard negative prompt phrases intended to broadly improve output quality across many different subject prompts.
Adoption and interfaces
The technique became widely known through open-source interfaces built around Stable Diffusion, which exposed a dedicated negative prompt field alongside the main prompt box and other conditioning tools such as ControlNet, a pattern subsequently adopted by many other image generation tools. Some commercial platforms, including Midjourney, instead use a special parameter appended to the ordinary prompt rather than a separate input field, while others fold negative-prompt-like exclusion into more general prompt weighting systems. As newer generation models have improved at following complex positive instructions, the prominence of separate negative prompt fields in user interfaces has declined relative to the early Stable Diffusion era, though the underlying concept remains a common part of Prompt engineering for visual generation.
Limitations
Negative prompts do not guarantee exclusion of the named concepts; because diffusion models work statistically rather than through hard constraints, an undesired element can still appear, particularly when the negative and positive prompts describe conflicting or overlapping regions of the model's learned distribution. Overloading a negative prompt with too many terms can also degrade overall image quality or push the output toward a generic, over-corrected style, leading many practitioners to keep negative prompts short and specific rather than exhaustive.