  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.
