Negation Prompting Is Weak: Instead, Explicitly Describe the Desired Behavior

Prompting with negation — "don't be verbose," "don't add caveats," "don't moralize" — is surprisingly ineffective. A detailed Reddit post breaks down why and offers concrete replacements that actually steer model behavior.

Negation Doesn't Cancel Topics

When you say "don't be wordy", the model still activates the concept of wordiness and writes around it, but doesn't truncate responses. Same for "don't add caveats" — the model generates caveats, then tries to negate them, resulting in verbose, hedged answers.

Positive Instructions Work

• Instead of "don't be wordy": "Respond in 1–2 sentences unless I ask for more."
• Instead of "don't moralize": "Give me a direct answer, treat caveats as optional."
• Instead of "don't use bullets": "Use plain prose, no lists."

Tone Leak from Closing Politeness

Ending a prompt with "thanks!" or "please." shifts the model's tone toward warmer and wordier responses. Neutral endings (just the instruction) yield neutral tones. The effect appears consistent across Opus 4.7 and Sonnet 4.6, and presumably in Haiku too.

Practical Takeaway

These aren't hacks — they're how instruction following actually works. Tell the model what you want, not what you don't want. Explicitly describe the desired output format and style, and keep the prompt tone-neutral if you want a neutral response.