FlowGPT: Exploring Domains, Output Modalities, and Goals of Community-Generated AI Chatbots

TL;DR

This paper analyzes FlowGPT, a platform for sharing AI chatbots, to understand the domains, output modalities, and goals of community-generated AI tools, highlighting common types and future research directions.

Contribution

It provides an initial exploration of the types, purposes, and characteristics of AI chatbots shared within a community platform, addressing a gap in understanding community-driven AI development.

Findings

Identification of common domains for AI chatbots

Analysis of output modalities used in community-shared chatbots

Insights into the goals and purposes of AI tools in the community

Abstract

The advent of Generative AI and Large Language Models has not only enhanced the intelligence of interactive applications but also catalyzed the formation of communities passionate about customizing these AI capabilities. FlowGPT, an emerging platform for sharing AI prompts and use cases, exemplifies this trend, attracting many creators who develop and share chatbots with a broader community. Despite its growing popularity, there remains a significant gap in understanding the types and purposes of the AI tools created and shared by community members. In this study, we delve into FlowGPT and present our preliminary findings on the domain, output modality, and goals of chatbots. We aim to highlight common types of AI applications and identify future directions for research in AI-sharing communities.