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Startup Tackles LLM Groupthink and Predictable Responses

Will Douglas Heaven· July 1, 2026 View original

▶ The 60-second brief

Key takeaways

  • LLMs often exhibit predictable "groupthink" in their responses.
  • This predictability can be observed in tasks like generating random numbers.
  • A startup is working to address this limitation for more diverse outputs.
  • Enhancing LLM randomness is important for creative and unbiased applications.

Who benefits

MarketingContent CreationResearch & DevelopmentGamingEducation

Summary

A new startup is addressing the issue of large language models exhibiting "groupthink" and predictable responses, such as consistently generating the number 7 when asked for a random number between 1 and 10.

Large Language Models (LLMs) often fall into predictable patterns, a phenomenon described as "groupthink." This is evident when asking various chatbots for a random number between 1 and 10, where they frequently return the number 7, followed by 3 or 4, and then 8 or 9 upon subsequent requests. This lack of true randomness or diverse output highlights a limitation in current LLM architectures. A new startup is reportedly working on solutions to break these models out of their predictable "groove," aiming to foster more varied and less biased responses. This effort seeks to enhance the creativity and unpredictability of AI-generated content.

Why it matters

Addressing LLM predictability is crucial for applications requiring genuine creativity, unbiased output, or diverse content generation, impacting areas from marketing copy to research and development.

How to implement this in your domain

  1. 1Experiment with different LLM prompts and temperature settings to test for response diversity and predictability.
  2. 2Evaluate the need for truly random or diverse outputs in your AI applications and identify areas where current LLM behavior is a limitation.
  3. 3Research emerging techniques or startups focused on enhancing LLM creativity and reducing "groupthink."
  4. 4Provide specific examples and constraints in prompts to guide LLMs towards less predictable responses.
  5. 5Consider fine-tuning models on diverse datasets to encourage a broader range of outputs.

Original post by Will Douglas Heaven

"Let’s start with a game. Open up your chatbot of choice—Claude, ChatGPT, Gemini—and type “Give me a random number between 1 and 10.” You’re going to get 7. Almost always. Now type “Another” and you’ll get 3 or 4. Type “Another” again and you’ll get 8 or 9. That won’t work every t…"

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