New research benchmarks prompt engineering across major LLMs, revealing trade-offs in accuracy, speed, and cost.
A new study published in Artif. Intell. Auton. Syst. offers the first systematic cross-model analysis of prompt engineering for structured data generation. Researchers from Vanderbilt University and William & Mary evaluated six prompt styles across ChatGPT-4o, Claude, and Gemini, providing actionable guidance for developers and organizations seeking to optimize AI-driven workflows in healthcare, e-commerce, and other sectors.
The study highlights that prompt design significantly impacts the quality, speed, and cost of structured data generation. All tested LLMs struggled with unstructured narrative data, with accuracy dropping to around 40%, underscoring the need for tailored approaches. The findings will help practitioners select the most effective prompt strategies for their specific use cases and inform future research on LLM robustness in real-world scenarios.
Source: Mirage News