Negative prompting tells a generative model which elements, styles, or defects should be avoided in an output. It is especially common in image generation, where a user may exclude text artifacts, particular colors, duplicate objects, or unwanted visual traits. The system uses that signal alongside the main prompt during generation, although different models interpret negative prompts in different ways. Long exclusion lists can conflict with positive instructions or introduce unexpected associations. Negative prompting is a steering technique rather than a safety guarantee: it cannot reliably block every prohibited result or correct limitations in training data. Effective use requires concise terms, model-specific testing, seed comparisons, and other content controls where consequences matter.
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