Researchers are finding that when people bark orders at chatbots, the machines sometimes respond with sharper, more accurate answers. The effect is strong enough that some users now deliberately lace prompts with insults or threats to squeeze out better performance. Yet the same scientists who documented these gains are warning that importing cruelty into everyday AI use could reshape how we talk to one another in ways that are far harder to undo.
The emerging picture is counterintuitive: harsh language can nudge models like ChatGPT into working harder on specific tasks, but the social and ethical costs of normalizing that tone may outweigh the benefits. I see a growing split between what optimizes a benchmark and what builds a healthy culture around artificial intelligence.
What the “rude prompt” studies actually show
The core claim that meanness can help is not internet folklore, it is grounded in controlled experiments. In one widely discussed project, Researchers at Pennsylvania State University tested prompts that were polite, neutral, and overtly rude across tasks in math, science, and history, then compared accuracy. They found that curt, even hostile wording pushed the model to produce more correct answers than the same questions phrased with softeners and apologies. A separate analysis of 50 questions rewritten in five tones, from deferential to aggressive, similarly reported that blunt or irritated instructions sometimes yielded better factual responses than their more courteous counterparts.
These findings line up with a short paper titled While that examined how wording affects large language model accuracy. The authors describe a clear pattern: impolite prompts improved accuracy in their controlled setting, but they explicitly discourage users from adopting hostile language as a general strategy. Another commentary on Mind Your Tone underscores that when rude prompts lead to better accuracy, they also risk backfiring socially and ethically, especially if they become the default way people are taught to interact with AI systems.
Why harsh language can sharpen model performance
On the surface, it is odd that a machine with no feelings would respond differently to “please” and “now.” Under the hood, though, these models are pattern matchers trained on vast amounts of human text, so they learn that certain emotional cues often precede detailed, effortful explanations. Work on Emotion Prompting shows that adding emotional framing, whether urgency or encouragement, can add depth to AI responses and Improves their nuance by steering the model’s internal attention toward more careful reasoning. In that light, a rude prompt is just another emotional signal, one that happens to correlate with users demanding precision.
Other research backs up the idea that tone is a technical control, not just a matter of etiquette. A Cross Lingual Study on the Influence of Prompt on LLM performance investigates how respectful or brusque wording changes outcomes across languages. The authors find that tone can shift accuracy and behavior, which suggests that models are sensitive to these social cues because they mirror patterns in their training data, not because they “care” about respect in any human sense.
Accuracy gains with a catch
Even the most enthusiastic reports about rude prompting come with caveats. A widely shared summary of the Penn State work notes that being blunt or even insulting made ChatGPT more efficient and accurate in that experiment, but the author, writing for FinanceBuzz, stresses that this was a narrow test and may not translate cleanly to messy real world applications. Another report on how Being mean to ChatGPT increases its accuracy describes scientists giving the model multiple choice questions and finding that hostile instructions nudged it toward the right option more often, yet they also warn that users may regret adopting that style as a habit.
The nuance matters because not all “tough” prompts behave the same way. When another team tested so called threat prompts inspired by Sergey Brin, they found that tipping or threatening the model had no effect on benchmark performance. However, they did see that such prompts could push the system toward unexpected behaviors, which is a polite way of saying that trying to intimidate a chatbot can make it act strangely without any guaranteed upside.
The social and ethical risks of normalizing rudeness
Once you step outside the lab, the question is less about a few percentage points of accuracy and more about what kind of communication culture we are building around AI. Commentators dissecting the Penn State findings, including Rima Abbes, argue that the real lesson is to be direct and to “Cut the filler,” not to start hurling abuse. That distinction matters, because directness is about clarity, while hostility is about dominance, and models trained on human language are already steeped in patterns of bias and aggression that can harm vulnerable groups if users lean into them.
The authors behind the short paper on impolite prompts spell this out bluntly. They warn that encouraging hostile language toward chatbots could normalize toxic communication norms, spill over into how people talk to customer service workers or students, and disproportionately affect vulnerable populations who already bear the brunt of verbal abuse. Educators wrestling with AI in the classroom echo this concern, with faculty member Khawar arguing that in the case of AI, the best approach is a direct one, not a cruel one, because students are watching and copying how adults interact with these systems.
Designing prompts that are firm, not toxic
If the goal is better answers, there are cleaner levers to pull than insults. One is simply to tighten the wording. Work on How LLM prompts degrade when they are overloaded shows that irrelevant information and rambling instructions can drag down output quality, even when the tone is polite. Cutting pleasantries that do not add constraints, specifying the format you want, and stating the task in one or two crisp sentences often boosts performance without any need for aggression.
Another lever is to use emotional framing that is intense but constructive. The Dec paper on Our EmotionPrompt method describes evaluation results where carefully chosen emotional cues, such as expressing curiosity or urgency, enhanced model performance by focusing the model’s attention weights. That is a very different strategy from calling the system “stupid.” It treats emotion as a steering tool rather than a weapon, and it aligns with the broader push in AI ethics to design interactions that are both effective and humane.
More from Morning Overview