Enhancing structured data generation with GPT-4o

Large language models (LLMs), such as GPT-4o, provide versatile techniques for generating and formatting structured data. However, prompt style plays a critical role in determining the accuracy, efficiency, and token cost of the generated outputs. This paper explores the effectiveness of three specific prompt styles–JSON, YAML, and Hybrid CSV/Prefix–for structured data generation across diverse applications. We focus on scenarios such as personal stories, receipts, and medical records, using randomized datasets to evaluate each prompt style's impact. Our analysis examines these prompt styles across three key metrics: accuracy in preserving data attributes, token cost associated with output generation, and processing time required for completion. By incorporating structured validation and comparative analysis, we ensure precise evaluation of each prompt style's performance. Results are visualized through metrics-based comparisons, such as Prompt Style vs. Accuracy, Prompt Style vs. Token Cost, and Prompt Style vs. Processing Time.