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.”

In the crypto world, reports of cyberattacks tend to focus mainly on the funds that vanish immediately. A new study by researchers at the Complexity Science Hub (CSH) now shows that the indirect consequences – such as falling token prices and eroding trust – can multiply the financial impact of such attacks, with investors bearing the brunt.

A team of researchers has built one of the most detailed open maps of emerging technologies yet assembled, allowing governments, companies and investors in the United States and worldwide to see what sits inside big fields like artificial intelligence and quantum computing, how fast each technology is growing and how deeply it is rooted in science.

Researchers at Universidad Carlos III de Madrid (UC3M) have developed a set of innovative methods and algorithms that improve the performance and precision of vehicle design through topological optimization, a mathematical technique that allows designs to be optimized by distributing materials efficiently. The results of the research, which these scientists have applied to the design of parts for a competition motorcycle to reduce its weight while maintaining performance, could have a major impact on sectors such as the automotive and aeronautics industries.