Negative Prompting: Specifying What to Avoid

Sometimes it's easier to specify what you don't want than what you do want. Negative prompting explicitly tells the model to avoid certain outputs, styles, or content. This technique is powerful for refining results and preventing common issues.

What Is Negative Prompting

Negative prompting adds constraints about what to avoid. You can specify unwanted styles, topics, formats, or characteristics. The model then generates outputs that satisfy your positive requirements while avoiding the negative constraints.

This is particularly useful when models tend to produce certain unwanted patterns. By explicitly stating what to avoid, you guide the model away from these patterns toward better outputs.

Common Use Cases

Avoid AI-sounding language: "Don't use phrases like 'in conclusion' or 'it's important to note.'" This helps produce more natural, human-like writing.

Prevent generic content: "Avoid clichés and generic statements." This pushes the model toward more specific, original content.

Exclude specific topics: "Don't mention X or Y topics." Useful when you want to focus on certain areas while avoiding others.

Best Practices

Be specific with negative constraints. "Don't be generic" is vague; "Avoid phrases like 'it's worth noting' and 'in today's world'" is actionable.

Combine positive and negative prompts. Tell the model what you want and what to avoid. This provides clear guidance in both directions.

Test negative prompts to ensure they work. Some negative constraints may not be effective, or may conflict with positive requirements. Iterate to find the right balance.