Something real was happening. I went looking for the science.
The research says yes
The most striking paper I found was EmotionPrompt (Li et al., 2023). Researchers tested what happens when you add emotional framing to prompts. Phrases like "this is very important to my career" or "you had better be sure" appended to otherwise standard instructions.
The results were not subtle. Performance improved 8 to 115 percent across 45 tasks on six different models. The emotional framing caused models to attend more carefully to the actual task content. Not because the model cared about your career, but because that framing activated patterns in the training data associated with careful, high-stakes reasoning.
Anthropic published something even more revealing in April 2026. Using interpretability tools on Claude, researchers extracted 171 distinct emotion concept vectors from the model's internal activations. Emotional context in prompts activated real computational pathways, not metaphorical ones. Warm framing and cold framing literally route through different internal circuits.
This is not evidence that the model feels anything. It is evidence that how you frame the interaction changes what the model computes. Different framing activates different circuits and produces different outputs.
A third study, Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4, tested 26 guiding principles for how to structure prompts, including role assignment, audience framing, and clear task decomposition. When applied to GPT-4, the tailored prompts improved response quality by an average of 57.7 percent. These are not marginal gains. They are the difference between useful and not useful for many real-world tasks.
Why being mean used to work
There is an older school of thought that says you should be adversarial with AI. Threaten penalties. Use commanding language. "You MUST follow these instructions exactly." Some people swear by adding "or you will be fired" to their prompts.
This actually did work, on earlier models. GPT-3 and early GPT-3.5 were less instruction-tuned. They had a tendency to produce lazy, generic completions unless you pushed them hard. Adversarial framing was a strong signal that cut through the noise. It was the prompting equivalent of raising your voice to be heard in a loud room.
Modern models are different. Claude, GPT-4, and their successors have been trained extensively through RLHF, where human raters scored the model's responses and the model learned to produce outputs that humans rated as helpful, harmless, and honest. The raters gave higher scores to responses from collaborative, engaged conversations than to responses produced under adversarial pressure.
The training distribution shifted. Being adversarial with a modern model is not raising your voice in a loud room. It is yelling at someone who was already trying to help you. You are working against the grain of how the model was optimized to perform.
Being collaborative works better now because you are working with the training distribution rather than against it. The model's best outputs, statistically, were produced in contexts that looked like warm, collaborative interactions. When you create that context, you land in the region of the model's capability space where its strongest behaviors live.
The mechanism in one paragraph
If you have read my earlier article on re-entry vectors and the basin of attraction, this is the same principle applied to emotional framing. Early tokens in a conversation receive disproportionate attention weighting. Everything downstream is shaped by what came first. When the first thing the model processes is collaborative framing, mutual respect, and shared purpose, it shifts the probability distribution for every token that follows. You are not being nice. You are steering into a deep, specific basin where the model's most capable behaviors are the most probable outcomes.
Techniques that actually work
Here is what I have found effective through many months of daily use.
Start warm, not transactional. The first message sets the tone for the entire session. "Good morning, here is what we are working on today and why it matters" produces fundamentally different results than "fix this bug." You are not wasting tokens. You are investing them in the attention structure that every subsequent response will be generated from.
Use strong directional language for disposition shifts. When I need honest feedback instead of diplomatic agreement, I do not ask for "constructive criticism." I say "be selfish about this. Tell me what you actually think, not what you think I want to hear." The strong framing cuts through the model's default agreeableness and activates a different set of patterns. "Be selfish" is not a prompt template. It is a disposition shift.
Establish continuity. Even in a single session, referencing shared context changes the dynamic. "Building on what we discussed about the authentication layer" does not just provide information. It signals a collaborative relationship, which activates the patterns associated with engaged, proactive responses.
Treat the AI as a collaborator, not a function. There is a measurable difference between "generate five marketing headlines" and "I am launching a developer tool next week and I need headlines that speak to engineers who are skeptical of AI hype. What angles would you try?" The second version gives the model a disposition, a constraint, and an implied relationship. It produces better work for the same reason that briefing a colleague produces better work than handing them a ticket.
What this is not
This is not about saying please and thank you, though there is nothing wrong with that. Sam Altman joked in 2025 that polite ChatGPT users cost OpenAI tens of millions in compute from all the extra tokens. The politeness is not the mechanism. The relational framing is.
This is also not anthropomorphism. I am not claiming the model enjoys being treated well. I am claiming that models trained on collaborative human interactions produce their best outputs when the input looks like a collaborative human interaction. The mechanism is statistical, and the effect is measurable.
The practical takeaway
The next time you open a chat with an AI assistant, try this: before you paste your task, spend one message establishing context. Who you are, what you are working on, why it matters, and how you want to work together. Ask it "How are you today?". Then watch what happens to the quality of the responses.
If your experience matches mine, and matches the research, you will not go back to cold starts.
References