The Prompt Report: A Systematic Survey of Prompting Techniques

Sander Schulhoff ,

Michael Ilie ,

Nishant Balepur ,

Konstantine Kahadze ,

Amanda Liu ,

Chenglei Si ,

Yinheng Li ,

Aayush Gupta ,

HyoJung Han ,

Sevien Schulhoff ,

Pranav Sandeep Dulepet ,

Saurav Vidyadhara ,

Dayeon Ki ,

Sweta Agrawal ,

Chau Pham ,

Gerson Kroiz ,

Feileen Li ,

Hudson Tao ,

Ashay Srivastava ,

Hevander Da Costa ,

Saloni Gupta ,

Megan L. Rogers ,

Inna Goncearenco ,

Giuseppe Sarli ,

Igor Galynker ,

Denis Peskoff ,

Marine Carpuat ,

Jules White ,

Shyamal Anadkat ,

Alexander Hoyle ,

Philip Resnik

LLM Other LLM NLP APGO

生成人工智能（GenAI）系统正在越来越多地部署在各行各业和研究领域。开发人员和最终用户通过提示或提示工程与这些系统进行交互。虽然提示是一个广泛研究的概念，但由于该领域的新生性质，存在术语冲突和对构成提示的本体论理解不足的问题。本文通过组装提示技术分类法并分析其使用，建立了对提示的结构化理解。我们提出了一个包含33个词汇术语的全面词汇表，一个包含58个仅文本提示技术的分类法，以及其他模态的40种技术。我们进一步对自然语言前缀提示的整个文献进行了元分析。

Generative Artificial Intelligence (GenAI) systems are being increasingly deployed across all parts of industry and research settings. Developers and end users interact with these systems through the use of prompting or prompt engineering. While prompting is a widespread and highly researched concept, there exists conflicting terminology and a poor ontological understanding of what constitutes a prompt due to the area's nascency. This paper establishes a structured understanding of prompts, by assembling a taxonomy of prompting techniques and analyzing their use. We present a comprehensive vocabulary of 33 vocabulary terms, a taxonomy of 58 text-only prompting techniques, and 40 techniques for other modalities. We further present a meta-analysis of the entire literature on natural language prefix-prompting.