Prompting Diverse Ideas: Increasing AI Idea Variance

38 Pages Posted: 12 Feb 2024

See all articles by Lennart Meincke

Lennart Meincke

University of Pennsylvania; The Wharton School

Ethan R. Mollick

University of Pennsylvania - Wharton School

Christian Terwiesch

University of Pennsylvania - Operations & Information Management Department

Date Written: January 27, 2024

Abstract

Unlike routine tasks where consistency is prized, in creativity and innovation the goal is to
create a diverse set of ideas. This paper delves into the burgeoning interest in employing
Artificial Intelligence (AI) to enhance the productivity and quality of the idea generation process.
While previous studies have found that the average quality of AI ideas is quite high, prior
research also has pointed to the inability of AI-based brainstorming to create sufficient
dispersion of ideas, which limits novelty and the quality of the overall best idea. Our research
investigates methods to increase the dispersion in AI-generated ideas. Using GPT-4, we explore
the effect of different prompting methods on Cosine Similarity, the number of unique ideas, and
the speed with which the idea space gets exhausted. We do this in the domain of developing a
new product development for college students, priced under $50. In this context, we find that (1)
pools of ideas generated by GPT-4 with various plausible prompts are less diverse than ideas
generated by groups of human subjects (2) the diversity of AI generated ideas can be
substantially improved using prompt engineering (3) Chain-of-Thought (CoT) prompting leads to
the highest diversity of ideas of all prompts we evaluated and was able to come close to what is
achieved by groups of human subjects. It also was capable of generating the highest number of
unique ideas of any prompt we studied.

Keywords: innovation, idea generation, creativity, creative problem solving, LLM, large-scale language models, AI, artificial intelligence, ChatGPT, idea space, prompt engineering

Suggested Citation

Meincke, Lennart and Mollick, Ethan R. and Terwiesch, Christian, Prompting Diverse Ideas: Increasing AI Idea Variance (January 27, 2024). Available at SSRN: https://ssrn.com/abstract=4708466 or http://dx.doi.org/10.2139/ssrn.4708466

Lennart Meincke (Contact Author)

University of Pennsylvania ( email )

Philadelphia, PA 19104
United States

The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

Ethan R. Mollick

University of Pennsylvania - Wharton School ( email )

The Wharton School
Philadelphia, PA 19104-6370
United States

Christian Terwiesch

University of Pennsylvania - Operations & Information Management Department ( email )

Philadelphia, PA 19104
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
5,285
Abstract Views
11,307
Rank
3,003
PlumX Metrics