Groundbreaking research compares prompt styles and LLMs for structured data generation - Unveiling key trade-offs for real-world AI applications
Peer-Reviewed Publication
Updates every hour. Last Updated: 2-Dec-2025 07:11 ET (2-Dec-2025 12:11 GMT/UTC)
Nashville, TN & Williamsburg, VA – 24 Nov 2025 – A new study published in Artif. Intell. Auton. Syst. delivers the first systematic cross-model analysis of prompt engineering for structured data generation, offering actionable guidance for developers, data scientists, and organizations leveraging large language models (LLMs) in healthcare, e-commerce, and beyond. Led by Ashraf Elnashar from Vanderbilt University, alongside co-authors Jules White (Vanderbilt University) and Douglas C. Schmidt (William & Mary), the research benchmarks six prompt styles across three leading LLMs to solve a critical challenge: balancing accuracy, speed, and cost in structured data workflows.
Structured data—from medical records and receipts to business analytics—powers essential AI-driven tasks, but its quality and efficiency depend heavily on how prompts are designed. “Prior research only scratched the surface, testing a limited set of prompts on single models,” said Elnashar, the study’s corresponding author and a researcher in Vanderbilt’s Department of Computer Science. “Our work expands the horizon by evaluating six widely used prompt formats across ChatGPT-4o, Claude, and Gemini, revealing clear trade-offs that let practitioners tailor their approach to real-world needs.”
Researchers at the University of Oulu have identified significant differences in mortality among widows and widowers from cohabiting versus married relationships, depending on the deceased partner’s cause of death. Accidental death increases the surviving partner’s mortality risk more than death from illness. Mortality rises particularly sharply among those who had been living in cohabitation.