A couple of days ago, the Swedish channel TV4 had a feature about the costs and environmental impact of polite phrases when chatting with LLM’s. Every prompt use processor power that needs energy.
A researcher claimed that it didn’t matter whether we are polite or not. But is this true? LLM learns from data created by humans. It also includes nuances in our communication
The tone you strike when talking to an LLM shapes not just the style but often the substance of its response. Below is an in-depth exploration of why polite framing steers these models toward more helpful, accurate, and contextually rich answers.
- The Human Mirror: LLMs Reflect Conversational Patterns
Large language models are trained on massive corpora of human text. Within that data, polite, cooperative dialogues dominate high-quality content such as books, articles, and well-moderated forums.
- When a prompt includes courtesies like “please” and “thank you,” it aligns closely with the model’s learned patterns for helpful exchanges.
- Curt or hostile language often appears in adversarial or off-topic contexts in the training set, pushing the model toward terseness, refusals, or boilerplate answers.
By mirroring politeness, you effectively prime the model to draw from sections of its training where collaboration and clarity were rewarded.
- Training Dynamics and Tone Correlation
During pre-training, models learn statistical associations between words and response patterns.
- Polite markers co-occur with elaborated explanations, examples, and structured responses.
- Negative or abrasive cues frequently correlate with defensive or truncated completions.
Think of it like a probability table: given a verbatim “Could you please…?”, the model has seen thousands of expanded, detail-rich replies that follow. Supply a brusque “Do this now,” and the highest-probability completion might be a loop of error handling or a bland refusal.
- Reinforcement Learning from Human Feedback (RLHF)
Post-training fine-tuning often involves RLHF, where human annotators rate model outputs on helpfulness, safety, and tone.
- Annotators consistently reward responses to polite prompts with higher scores.
- Over time, the model internalizes that courteous user queries yield more positive reinforcement and thus biases itself toward cooperating when addressed politely.
This creates a feedback loop: polite user tone → high annotator score → model amplifies similar tone in future.
- Prompt Framing and Priming Effects
Politeness isn’t just window dressing—it shapes the model’s understanding of your objective.
- Framing a question as a request for collaboration (“Could you help me understand…?”) cues the model to take an explanatory stance.
- Abrupt commands (“Explain X”) might prime it for terse bullet points or an outline lacking narrative depth.
You’re not tricking the model; you’re steering it into the ‘helpful collaborator’ mindset embedded in its training.
- Safety Filters and Tone Detection
Modern LLMs incorporate safety and content-filter subsystems that monitor toxicity, hate speech, or harassment.
- A hostile prompt may trigger more aggressive scrutiny, leading to partial redactions or deflected answers.
- Polite language generally passes through filters unimpeded, so the model feels “safe” to go deeper.
In effect, tone modulates not just stylistic choices but also which internal submodels get activated.
- Empirical Comparison: Polite vs. Curt
| Aspect | Polite Prompt | Curt Prompt |
| Response Length | Expanded (≥150 words) | Concise (<100 words) |
| Use of Examples | Multiple real-world scenarios | Rare, sometimes none |
| Narrative Flow | Coherent story-like structure | Disconnected bullet points |
| Safety Check Impact | Minimal | Heightened filter sensitivity |
Running side-by-side experiments with identical questions but different tones will repeatedly confirm these trends.
- Best Practices for Polite Prompting
- Begin with a greeting or “please.”
- Specify context or constraints clearly.
- Frame tasks as a collaboration: “I’d appreciate your thoughts on…”
- End with a brief “thanks.”
Example:
Hi Copilot, could you please walk me through the main steps of designing a REST API? Thanks!
This simple structure unlocks more thorough, nuanced, and coherent answers.