Generative Artificial Intelligence (GenAI) systems are increasingly being deployed across diverse industries and research domains. Developers and end-users interact with these systems through the use of prompting and prompt engineering. Although prompt engineering is a widely adopted and extensively researched area, it suffers from conflicting terminology and a fragmented ontological understanding of what constitutes an effective prompt due to its relatively recent emergence. We establish a structured understanding of prompt engineering by assembling a taxonomy of prompting techniques and analyzing their applications. We present a detailed vocabulary of 33 vocabulary terms, a taxonomy of 58 LLM prompting techniques, and 40 techniques for other modalities. Additionally, we provide best practices and guidelines for prompt engineering, including advice for prompting state-of-the-art (SOTA) LLMs such as ChatGPT. We further present a meta-analysis of the entire literature on natural language prefix-prompting. As a culmination of these efforts, this paper presents the most comprehensive survey on prompt engineering to date.
The Prompt Report: A Systematic Survey of Prompting Techniques
A taxonomy and meta-analysis are provided for prompting techniques and vocabulary in GenAI systems, with a focus on natural language prefix-prompting.
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- 2024
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- arXiv 2024
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- 31
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- arxiv.org/abs/2406.06608v5ARXIV-DEFAULT
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31Shyamal AnadkatChenglei SiYinheng LiSander SchulhoffAshay SrivastavaAlexander HoylePhilip ResnikMichael IlieNishant BalepurKonstantine KahadzeAmanda LiuAayush GuptaHyojung HanSevien SchulhoffPranav Sandeep DulepetSaurav VidyadharaDayeon KiSweta AgrawalChau PhamGerson KroizFeileen LiHudson TaoHevander Da CostaSaloni GuptaMegan L. RogersInna GoncearencoGiuseppe SarliIgor GalynkerDenis PeskoffMarine CarpuatJules White