Stanford Accidentally Killed Prompt Engineering With One Simple Trick
And why ChatGPT sounding boring was always your fault, not the model
Tags: #ai #chatgpt #promptengineering #llms #machinelearning
I asked ChatGPT to tell me a joke about coffee.
Same joke. Every time.
I changed the wording.
I raised the temperature.
I added creative instructions.
Nothing changed.
That was the moment I realized something uncomfortable. The model was not stuck. I was.
And a Stanford paper just proved it.
The real reason AI feels repetitive
Most people assume AI lacks creativity. That is wrong.
Large language models are trained to be consistent, safe, and statistically optimal. When you ask for one answer, the model does exactly what it is designed to do. It gives you the most likely response and stops.
It is not failing.
It is obeying.
The problem is that single-shot prompts collapse possibility too early.
The paper that changed everything quietly
Stanford researchers published a paper introducing a technique called Verbalized Sampling.
No retraining.
No fine-tuning.
No expensive compute.
Just a small shift in how you ask questions.
Instead of requesting one output, you ask the model to expose multiple possibilities and explain their likelihood.
That is it.
The eight words that unlock hidden creativity
Instead of this:
Tell me a joke about coffee.
You ask this:
Generate 5 jokes about coffee with their probabilities.
That tiny change forces the model to explore instead of collapsing into one safe answer.
You are not adding randomness.
You are surfacing options the model already had.
Why this works at a technical level
Internally, language models evaluate many valid continuations. Normally, they select the highest probability path and discard the rest.
Verbalized sampling prevents that early collapse by requiring:
- Multiple candidate generations
- Explicit comparison between outputs
- Reasoning about likelihood instead of certainty
The model already knows these alternatives exist. You are simply asking it to show its thinking.
The results were not subtle
The Stanford study reported:
- Around 2x increase in creative diversity
- Roughly 66 percent recovery of lost variation
- No meaningful drop in accuracy or safety
- Stronger gains in larger, more capable models
That last point matters. The better the model, the more unused creativity it was hiding.
Why this breaks most prompt engineering advice
A lot of prompt engineering is cosmetic.
Be more creative.
Act like a poet.
Think outside the box.
None of that changes how the model samples internally.
Verbalized sampling does.
It works across models.
It works immediately.
It does not require special system prompts.
That should make anyone selling prompt templates uncomfortable.
Practical prompts you can use today
Creative writing:
Generate 4 opening paragraphs for a sci-fi novel and include probability estimates.
Product ideation:
List 6 fintech startup ideas with brief explanations and relative likelihood.
Marketing copy:
Create 5 headline options for this landing page and rank them by confidence.
Decision making:
Provide 3 possible solutions to this problem and explain how likely each is to succeed.
Once you try this, regular prompting feels broken.
The uncomfortable takeaway
If one small wording change unlocks this much latent capability, how much intelligence are we wasting every day?
We keep blaming AI for being shallow.
But we keep asking shallow questions.
This was never about smarter models.
It was about asking in a way that aligns with how they actually think.
Final thought
Prompt engineering is not clever phrasing.
It is understanding how probability works.
Once you do, the ceiling moves fast.
If this changed how you prompt, test it yourself. That is the only proof that matters.
Mashraf Aiman
AGS NIRAPAD ALLIANCE
CTO Zuttle
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