Since the advent of large language models (LLMs), prompt engineering has been a crucial step for eliciting desired responses for various Natural Language Processing (NLP) tasks. However, prompt engineering remains an impediment for end users due to rapid advances in models, tasks, and associated best practices. To mitigate this, Automatic Prompt Optimization (APO) techniques have recently emerged that use various automated techniques to help improve the performance of LLMs on various tasks. In this paper, we present a comprehensive survey summarizing the current progress and remaining challenges in this field. We provide a formal definition of APO, a 5-part unifying framework, and then proceed to rigorously categorize all relevant works based on their salient features therein. We hope to spur further research guided by our framework.
@article{arxiv.2502.16923,
title = {A Systematic Survey of Automatic Prompt Optimization Techniques},
author = {Kiran Ramnath and Kang Zhou and Sheng Guan and Soumya Smruti Mishra and Xuan Qi and Zhengyuan Shen and Shuai Wang and Sangmin Woo and Sullam Jeoung and Yawei Wang and Haozhu Wang and Han Ding and Yuzhe Lu and Zhichao Xu and Yun Zhou and Balasubramaniam Srinivasan and Qiaojing Yan and Yueyan Chen and Haibo Ding and Panpan Xu and Lin Lee Cheong},
journal= {arXiv preprint arXiv:2502.16923},
year = {2025}
}