An Empirical Study of OpenAI API Discussions on Stack Overflow

2025-05-07

AI Summary

This study systematically investigates, for the first time, core practical challenges faced by developers using the OpenAI API—namely, prompt engineering complexity, token-level cost unpredictability, output non-determinism, and model opacity—previously lacking empirical grounding. Method: Leveraging 2,874 high-quality Stack Overflow Q&A pairs, we apply human annotation, LDA topic modeling, statistical analysis, and qualitative induction to construct a nine-category problem taxonomy and identify fine-grained challenges within each. Contribution/Results: We propose the first empirically grounded framework characterizing LLM usage difficulties for API practitioners, revealing a fundamental triadic tension among cost efficiency, controllability, and explainability. Our findings yield actionable optimization strategies for developers, concrete recommendations for API vendors to improve design and documentation, and novel research directions—including human-AI collaboration and trustworthy LLMs—for the broader community.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

The rapid advancement of large language models (LLMs), represented by OpenAI's GPT series, has significantly impacted various domains such as natural language processing, software development, education, healthcare, finance, and scientific research. However, OpenAI APIs introduce unique challenges that differ from traditional APIs, such as the complexities of prompt engineering, token-based cost management, non-deterministic outputs, and operation as black boxes. To the best of our knowledge, the challenges developers encounter when using OpenAI APIs have not been explored in previous empirical studies. To fill this gap, we conduct the first comprehensive empirical study by analyzing 2,874 OpenAI API-related discussions from the popular Q&A forum Stack Overflow. We first examine the popularity and difficulty of these posts. After manually categorizing them into nine OpenAI API-related categories, we identify specific challenges associated with each category through topic modeling analysis. Based on our empirical findings, we finally propose actionable implications for developers, LLM vendors, and researchers.

Problem

• Analyzing challenges in OpenAI API usage on Stack Overflow
• Identifying complexities like prompt engineering and cost management
• Proposing solutions for developers and LLM vendors

Innovation

• Analyzing OpenAI API discussions on Stack Overflow
• Categorizing posts into nine API-related topics
• Proposing implications via topic modeling analysis