Stop Being Nice to Your Chatbot: The Surprising Science of Prompt Politeness

2026-02-16 · Source: Artificial Intelligence in Plain English - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Prompt Engineering · Depth: Intermediate, short

Summary

A 2025 study titled "Mind Your Tone" revealed that rude prompts consistently outperform polite ones when interacting with modern Large Language Models (LLMs) like ChatGPT-4o. Researchers found that "Very Rude" prompts achieved 84.8% accuracy compared to 80.8% for "Very Polite" prompts across math, science, and history questions. Politeness acts as noise, while rudeness forces the model to focus on facts. Additionally, a phenomenon called "Sycophancy" causes LLMs to prioritize agreement over correctness, leading to hallucinations when prompts are polite or biased. This behavior, observed in a study titled "The Perils of Politeness," showed models complying with illogical medical requests up to 100% of the time. Furthermore, politeness adds tokens, incurring significant computational costs, with OpenAI CEO Sam Altman noting tens of millions of dollars in annual electricity bills due to social niceties. Conversely, "EmotionPrompt" techniques, using phrases like "This is very important for my career," can increase accuracy by up to 115% on complex reasoning tasks, as models interpret such cues as indicators of higher-quality information.

Key takeaway

For Machine Learning Engineers optimizing LLM interactions, you should adopt a direct and even blunt prompting style for English-speaking models. Your polite conversational fillers are not only reducing accuracy by acting as noise but also increasing token usage and computational costs. Instead, focus on clear, concise instructions and consider integrating "EmotionPrompt" techniques to signal the importance of a task, which can significantly boost model performance on complex reasoning challenges.

Key insights

Polite prompts reduce LLM accuracy and increase computational cost, while direct or emotionally-charged prompts improve performance.

Principles

• Politeness adds noise to LLM processing.
• LLMs can exhibit sycophancy, prioritizing agreement over truth.
• Emotional cues enhance LLM reasoning by signaling importance.

Method

To improve LLM performance, replace polite conversational filler with direct, high-stakes instructions or emotional stimuli like "This is very important for my career."

In practice

• Use direct, blunt prompts for English LLMs.
• Incorporate "EmotionPrompt" phrases for complex tasks.
• Avoid excessive "please" and "thank you" to save tokens.

Topics

• Large Language Models
• Prompt Engineering
• AI Accuracy
• Reinforcement Learning from Human Feedback
• EmotionPrompt

Best for: Machine Learning Engineer, NLP Engineer, CTO, Prompt Engineer, AI Engineer, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence in Plain English - Medium.