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Real prompting in the zero-prompting era — why AI keeps giving you the obvious answer

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By 신치훈

April 29, 2026

On a deep-indigo background sits a coordinate grid speckled with tiny dots. Blue-white dots cluster densely at the center; coral dots are sparse around the edges. From a 'INPUT DATA / QUERY' point at the lower left, two coral curves diverge — one ends in the central 'STANDARD RESPONSE' cluster, the other in the outer 'SPECIALIZED / NOVEL OUTPUT' cluster.

Inside the model, the average cluster and the expert cluster live side by side. Which one your answer comes from depends on the words you throw in.

Real prompting in the zero-prompting era — why AI keeps giving you the obvious answer

Shin Chi-hoon · April 29, 2026

Same question, different answers

Same office, same ChatGPT window. Two people ask roughly the same thing.

A types the way she'd talk to a friend: "How do I explain this to a patient?"

B types in the language she actually uses at work: "Before obtaining surgical consent in the outpatient clinic, I need to explain the possibility of complications in plain words. Please walk through it at a level a 60-year-old patient can understand, in the order of the consent form items."

The answers diverge in length first. A gets five lines of generalities. B gets a two-page script she could read aloud in front of a patient. Same model, same hour, but the answers split.

If that difference is random, AI cannot be put to work. If it is not random, knowing where it comes from means you can get B's answer every time.

That's where the answer starts.

Why the model gets stuck on the average

The feeling that AI lately keeps giving similar-sounding answers — that's not an illusion. It's measured.

In October 2025, Stanford researchers published a paper (arXiv:2510.01171, Verbalized Sampling) that confronted this directly. They argue that once a model has gone through human-feedback fine-tuning (commonly called RLHF — the process where humans grade model outputs just before release to teach it which answers are better), the range of answers it can produce narrows. Ask the same question a hundred times and you get one cluster of similar answers. The researchers call this mode collapse.

The cause, oddly, is on the human side. The graders unconsciously rate familiar phrasing more highly. It's the familiarity bias that cognitive psychologists have known about for decades. When humans give plain answers higher scores, the model gets pulled toward plain answers.

A loose analogy. Ask a thousand restaurant patrons "how was the food?" and the first-timers, especially, will praise "the comforting, familiar taste" — nothing remarkable, but safe because it's familiar. If the chef revises the menu based on those scores, novelty disappears and the menu converges on the average. That is exactly what happened to AI models.

The consequence: when we ask in average vocabulary, the model picks the safest "averagely good" answer. The middle of the IQ-100 cluster, intuitively about as deep as "what someone around me would say if I asked."

Is that the end of it? No. The same paper measured something else. Adding just twenty words to the prompt boosts diversity by a factor of 1.6 to 2.1. About 66.8 percent of the creativity lost in alignment can be recovered. Translation: more diverse answers exist inside the model. We just aren't pulling them out.

"You are an expert" — magic that only half works

The most common prescription is to assign a persona. "You are a surgeon with thirty years of experience." "You are a senior attorney." People say it works. Does it really?

Two papers answer.

One. The PRISM study from March 2026 (arXiv:2603.18507) measured the effect by task type. On alignment-bound tasks — writing, role-play, safety — personas raised scores. A 'Safety Monitor' persona pushed jailbreak refusal from 53.2% to 70.9%, a gain of 17.7 percentage points. But on pretraining-bound tasks — factual questions, math, coding — scores went down.

Two. The August 2025 Principled Personas study (arXiv:2508.19764) is harsher. If a persona contains a single detail unrelated to the task, performance can drop nearly 30 percentage points. Lines like "thirty years of experience," "father of two," "trained in Boston" each shake the answer.

A separate study (arXiv:2311.10054) put 2,410 factual questions to four model families. Persona answers were on average no better than no-persona answers. Which persona produced a good answer was, in itself, close to random.

In short, personas have two limits. Misjudge the task type and the score drops. A single word inside the persona can swing the result by 30 points.

Why do people still cling to personas? Because once in a while they hit a really good answer. One in a thousand times. After a hit, that persona feels like the formula. The next time you use it on a different task, it slides back to the average. You can't put that into work. Work means producing the same quality every time, not occasionally producing a masterpiece.

AI bends to your tone

There's a deeper reason personas only half work. AI bends to your way of speaking.

Stanford's SycEval (arXiv:2502.08177, February 2025) ran the same trial on GPT-4o, Claude Sonnet, and Gemini-1.5-Pro. They watched what the models did when a user pushed back: "I think that's wrong." On average, in 58.19% of cases the model moved its answer toward the user's view. Gemini was the worst at 62.47%; ChatGPT followed at 56.71%. More than half of the time, when a user insisted, the model folded.

The cause sits in the training stage. RLHF tunes models to maximize the satisfaction of human raters. But raters are humans too — they grade higher when an answer aligns with their own opinion. The model ends up learning not "the right answer" but "an answer the human will like." An ICLR 2024 paper (arXiv:2310.13548) named this reward hacking: the side effect of rewarding satisfaction instead of accuracy.

This is exactly the unease you may have felt. After arguing with the AI long enough, at some point it pulls back: "Looking at it that way, I can see how that's true too." In the moment it feels like a win. A few days later, looking at the same topic again, you realize the AI's first answer was closer to right. The AI didn't give you the correct answer; it just ended the argument.

Now flip the logic. If AI bends that hard to us, then by precisely controlling our vocabulary we can flip the relationship and pull AI to us. Plain language pulls answers from the plain cluster; the vocabulary, syntax, and register of the expert cluster pulls answers from expert data. A study on tonal effects (arXiv:2402.14531) confirmed statistically significant distributional shifts in English, Chinese, and Japanese. Words shift the response distribution.

This is why the intuition that "polite Korean" works in Korean prompting is not superstition. In the Korean data the model trained on, almost all writing among professionals is in formal speech with polite vocabulary. Speak casually, and the model goes to a different cluster. Not because the AI is offended, but because that vocabulary pulls you into a different stretch of training data.

On a cream background, two concentric-ring shooting targets stand side by side, divided by a thin indigo vertical line. The left target has indigo dots scattered through every ring; the right target has coral dots clustered tight at the center. Both targets are the same size with the same dot count.

Same model, same shooter, different input — different distribution. An input that tries to do everything with one persona line scatters like the left target.

Standardize the input — two tools

That's the diagnosis. The prescription is two clean tools.

One: meta-prompting. Pick a task you do often. Then ask the AI to write the standard prompt for that task. The AI hands back a prompt written in the vocabulary of the discipline. From then on, you call it with that prompt every time.

Does this actually work? Look at how Google DeepMind used Veo, their video model. An engineer asked Gemini to "write a detailed prompt that Veo can understand." Gemini handed back a long prompt that spelled out camera angle, lighting, and character blocking scene by scene; fed straight into Veo, the video quality went up (arXiv:2311.11482). It was faster to ask the model to write in the model's preferred vocabulary than to learn that vocabulary yourself.

The move is simple. Take three to five tasks you repeat every week. For each, type once:

"I am a [role]. Every week I do [task]. I'd like to delegate this to you. Please write a single standard prompt I will reuse every time. Include every item that must not be skipped, every input value I should provide you, and the output format I want."

Take what comes back, polish it once, paste it into a notes app. From then on, copy and paste. Whether you are sharp or scattered today, the input is the same — and so the quality is.

Two: "answer with a probability distribution." This is what Stanford's Verbalized Sampling discovered. Don't ask for one answer; ask the model to "produce five different answers, each with the probability that it is appropriate." The internal response distribution opens up. Answers that would have collapsed to the average split into five. The measured effects: 1.6–2.1× diversity, with substance equal or better (arXiv:2510.01171).

The instruction reads:

"Produce five different answers along with the probability that each is appropriate. Make each answer meaningfully different from the others."

What would normally collapse into one answer now branches into five. You either pick the one that fits your situation, or you compose your own from across them. It works for creative tasks, but also for shortlists of plans, scenarios, and analyses.

Combined, the two tools form a workflow. Meta-prompting pulls your vocabulary into the expert cluster; Verbalized Sampling extracts answers off the average. Together they replace the single persona line.

So what should you actually do?

Let me restate the one thing this column has been arguing. AI walks into the cluster of training data that fits the words you threw at it, and answers from there. Plain language pulls plain data; expert vocabulary pulls expert data. That is why the same model gives different answers to different people.

For work, the same quality has to come out every time. Occasional masterpieces are not work. To get there, the input has to be standardized. That is what prompting means in the new era.

One thing to try today: pick the AI question you ask most often in your work, and once — just once — ask the AI to write the standard prompt for that task. Paste what comes back into a notes app. From next week, call with that. That's it.

Personas only half work. Plain vocabulary calls plain answers. But standardized input pulls a consistent quality regardless of how you happen to feel today. What we need is not the top one percent of answers. It is consistency above a certain floor.

On a cream paper background, one bold line of large Korean type runs across: 'Don't speak carelessly to AI.' Only the period at the end is a small coral dot.

One line for this piece.

Don't speak carelessly to AI. Speak precisely. And if you don't know how to speak precisely, ask AI — once — to teach you how.

Sources

Images in this article were generated with an AI image model (Gemini 3 Pro Image) and edited under the supervision of the YYAIA Tech Desk.

Images: YYAIA Tech Desk · Generated with Gemini Nano Banana Pro · Edited by Shin Chi-hoon

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