Deciphering Human-AI Interactions: A Data-Driven Analysis of User Prompting Behaviors in Large Language Models

This is the authors' abstract. We don't add key points for this paper.

As artificial intelligence (AI) becomes an integral part of human-computer interaction, understanding how users communicate with Large Language Models (LLMs) is crucial for optimizing their effectiveness. This study explores real-world user prompts collected from platforms such as ShareGPT and Midjourney, analyzing trends in prompt formulation, structure, and effectiveness. We examine the role of Zero-Shot and Few-Shot prompting techniques in guiding AI responses and uncover patterns in user engagement with generative models. By identifying common strategies, challenges, and areas for improvement, our research provides insights into designing more intuitive AI interfaces and enhancing prompt engineering practices. These findings contribute to the broader field of human-AI collaboration, offering implications for AI model refinement and usability advancements in natural language processing systems.