Voices from the Frontier: A Comprehensive Analysis of the OpenAI Developer Forum
Xinyi Hou, Yanjie Zhao, Haoyu Wang

TL;DR
This paper analyzes the OpenAI Developer Forum to understand developer experiences, challenges, and engagement patterns, providing insights and recommendations to improve AI-powered application development and community support.
Contribution
It offers a comprehensive quantitative and qualitative analysis of forum data, including a taxonomy of developer concerns and engagement trends, advancing understanding of developer needs in AI application development.
Findings
Identified key developer concerns and challenges in AI application development.
Mapped engagement patterns and popularity trends across forum topics.
Provided targeted recommendations to address developer challenges.
Abstract
OpenAI's advanced large language models (LLMs) have revolutionized natural language processing and enabled developers to create innovative applications. As adoption grows, understanding the experiences and challenges of developers working with these technologies is crucial. This paper presents a comprehensive analysis of the OpenAI Developer Forum, focusing on (1) popularity trends and user engagement patterns, and (2) a taxonomy of challenges and concerns faced by developers. We first employ a quantitative analysis of the metadata from 29,576 forum topics, investigating temporal trends in topic creation, the popularity of topics across different categories, and user contributions at various trust levels. We then qualitatively analyze content from 9,301 recently active topics on developer concerns. From a sample of 886 topics, we construct a taxonomy of concerns in the OpenAI Developer…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOpen Education and E-Learning · E-Learning and Knowledge Management · Research Data Management Practices
