In the context of generative models, what do negative prompts do?

In generative models, negative prompts are used to specify content that should be excluded from the generated output. When a negative prompt is engaged, it instructs the model to avoid certain words, themes, or ideas, effectively refining the output to align more closely with the user's intent or desired parameters. This can be particularly useful when the user has specific constraints or criteria that should not be incorporated into the generated text. For example, if someone is generating images and wants to ensure that a particular color is absent, a negative prompt can direct the model to exclude that color, allowing for more tailored and relevant results.

The other options address different functionalities of prompts within generative models. For instance, encouraging creativity would involve positive prompts that stimulate imaginative output, while enhancing diversity and guiding toward particular themes relate more to the overall structure of the prompts rather than exclusion directives. Hence, specifying what should be excluded is indeed the primary role of negative prompts in this context.