Prompt Engineering
Prompt engineering is the art and science of crafting effective instructions—prompts—to guide large language models (LLMs) towards desired outputs. Current research focuses on developing automated methods for prompt optimization, exploring techniques like chain-of-thought prompting, and adapting prompts to specific LLMs and tasks (e.g., code generation, question answering, medical image analysis). This field is significant because effective prompt engineering dramatically improves the accuracy, efficiency, and reliability of LLMs across diverse applications, ranging from healthcare and education to software development and scientific research.
Papers
LLMs for Enhanced Agricultural Meteorological Recommendations
Ji-jun Park, Soo-joon Choi
Effects of a Prompt Engineering Intervention on Undergraduate Students' AI Self-Efficacy, AI Knowledge and Prompt Engineering Ability: A Mixed Methods Study
David James Woo, Deliang Wang, Tim Yung, Kai Guo
Enhancing Agricultural Machinery Management through Advanced LLM Integration
Emily Johnson, Noah Wilson