Chinese researchers have taken a fresh look at one of the biggest challenges in precision manufacturing: understanding and controlling the many different errors that affect the accuracy of machine tools. Their review, published in the International Journal of Extreme Manufacturing, explains why these errors are becoming increasingly complex to manage and how new technologies can help.

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.”

Asia-led, “sovereign-by-design” platform built for secure, decentralised pathogen intelligence-sharing across borders aims to break data silos and provide faster “time to actionable insight” of outbreaks, from detection to control measures being in place.