Can an AI Model Give You a Different Idea? | BymorningCan an AI Model Give You a Different Idea?<br>Ask an AI model for ten ideas and you will rarely get ten. Most models only give three to five; the rest are repeats.

This is called idea-mode collapse. The model picks a likely answer and keeps giving it back. That is one reason AI writing can be dull even when it reads well. It also blocks one of the best uses of AI. Good products, plans, and stories come from trying many ideas first. A model can dig into one idea fast, but it cannot give you many.

So how can you get a model to give you more ideas?

There are common techniques and a growing body of research, but in my own work they<br>rarely seemed to help. So I ran an experiment to find out which ones work.

Counting repeated ideas<br>To count ideas I used three rulers. A panel of models judged whether two answers expressed the same core idea. A fixed embedding method provided a second, fully repeatable count. I also adjusted the panel estimates using my own labels on 100 blind pairs, confirming the other methods are viable.

For each prompt, I generated ten answers and reduced each to a short, neutral statement of its main idea. I then grouped statements that expressed the same concept. If A matched B and B matched C, all three entered the same group. The number of groups was the idea count. The full experimental settings — models, prompts, generation parameters, and the counting pipeline — are in the appendix.

Example prompt

Propose one angle for an essay about why people procrastinate on creative projects.

The model panel agreed with my labels on 74% of the blind pairs. Models judging one another agreed at a similar rate, around 79%. Near the boundary, reasonable judges often disagreed about whether two ideas were truly different. The figures below are therefore broad estimates, not exact scores (more on this in Limits).

Distinct ideas per 10 samples

Embedding

Human-adjusted

Panel

Distinct ideas

MiniMax M3

Kimi K2.6

Qwen 3.7

GLM 5.2

Claude Opus

DeepSeek Pro

Claude Sonnet

GPT Terra

GPT Sol

Claude Haiku

I found no simple relationship between model size and idea diversity. This ran against my intuition — I expected bigger models to need less help. Haiku was last, while the reasoning-heavy MiniMax and Kimi models were near the top, but the ceiling was universal: no model came close to filling ten samples with ten different ideas. And as the methods below show, the strongest models gained the most from feedback, rather than not needing feedback in the first place. Collapse looks like a training artifact all models share, not a capability gap that guidance compensates for.[1]

The same answers keep returning<br>The collapse is easy to see on simple probes. I asked six models to pick a random number between 1 and 10. All six chose 7 most often, and five chose it on at least 95% of 200 draws, even when rephrasing the request. Asked for a random occupation, Claude Sonnet said "lighthouse keeper" 200 times out of 200.

Perhaps this shouldn't be surprising; LLMs are designed to answer questions with a single answer really well; the tradeoff is they are too narrow when the prompt admits many valid answers.[1]

This extends to creative tasks, where the same idea keeps winning. For example, these two responses from Claude are worded very differently, but express the same idea:

"Procrastination preserves ideas in their perfect imagined form rather than risking imperfect reality"

"People procrastinate on creative projects to preserve their infinite potential rather than risk disappointing reality."

Could we escape idea collapse by sending the same prompt to several different models? No. On 11 of the 15 prompts, most of the ten models gave the same most-common idea. Sometimes all ten agreed, and every model answered the prompt about isolated remote workers with some version of an ambient audio-visual presence app. Different models return the same idea in different words, so switching models does not buy you more diversity.

Model families do differ, but only in their rare ideas. Just 39 of the 340 idea groups (11.5%) held answers from more than one model family; counted prompt by prompt, so each prompt weighs equally, the share was about 21%. Split by region, 148 groups came only from Western labs, 155 only from Chinese labs, and 37 mixed the two. Average embedding similarity misses this gap: it was about 0.165 both within and across model families, showing no East/West distance at all. The average misses it because it counts every pair of answers, and most pairs are far apart; a group forms only when answers sit close together. Doshi and Hauser (2024)[5] found the same pattern: writers with AI access wrote more creative stories one by one, but their stories looked more alike as a set.

Changing the prompt<br>So how much can you improve diversity by changing the prompt itself? I tried some of the standard advice, and found that a method could change the look or...