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
Fault Diagnosis in Power Grids with Large Language Model
Liu Jing, Amirul Rahman
RB-SQL: A Retrieval-based LLM Framework for Text-to-SQL
Zhenhe Wu, Zhongqiu Li, Jie Zhang, Mengxiang Li, Yu Zhao, Ruiyu Fang, Zhongjiang He, Xuelong Li, Zhoujun Li, Shuangyong Song
Toward accessible comics for blind and low vision readers
Christophe Rigaud (L3I), Jean-Christophe Burie (L3I), Samuel Petit (Comix AI)
Addressing single object tracking in satellite imagery through prompt-engineered solutions
Athena Psalta, Vasileios Tsironis, Andreas El Saer, Konstantinos Karantzalos
Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation
Tianyu Wang, Nianjun Zhou, Zhixiong Chen