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
Demystifying Chains, Trees, and Graphs of Thoughts
Maciej Besta, Florim Memedi, Zhenyu Zhang, Robert Gerstenberger, Guangyuan Piao, Nils Blach, Piotr Nyczyk, Marcin Copik, Grzegorz Kwaśniewski, Jürgen Müller, Lukas Gianinazzi, Ales Kubicek, Hubert Niewiadomski, Aidan O'Mahony, Onur Mutlu, Torsten Hoefler
Towards Goal-oriented Prompt Engineering for Large Language Models: A Survey
Haochen Li, Jonathan Leung, Zhiqi Shen
Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
Tal Ridnik, Dedy Kredo, Itamar Friedman
Machine Translation with Large Language Models: Prompt Engineering for Persian, English, and Russian Directions
Nooshin Pourkamali, Shler Ebrahim Sharifi
PRewrite: Prompt Rewriting with Reinforcement Learning
Weize Kong, Spurthi Amba Hombaiah, Mingyang Zhang, Qiaozhu Mei, Michael Bendersky
More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering
Bingsheng Yao, Guiming Chen, Ruishi Zou, Yuxuan Lu, Jiachen Li, Shao Zhang, Yisi Sang, Sijia Liu, James Hendler, Dakuo Wang
To be or not to be? an exploration of continuously controllable prompt engineering
Yuhan Sun, Mukai Li, Yixin Cao, Kun Wang, Wenxiao Wang, Xingyu Zeng, Rui Zhao
Do Physicians Know How to Prompt? The Need for Automatic Prompt Optimization Help in Clinical Note Generation
Zonghai Yao, Ahmed Jaafar, Beining Wang, Zhichao Yang, Hong Yu