The Prompt Report: A Systematic Survey of Prompt Engineering Techniques
This work addresses the need for a unified framework in prompt engineering for developers and researchers, though it is incremental as it synthesizes existing knowledge rather than introducing new methods.
The paper tackles the problem of fragmented terminology and understanding in prompt engineering by establishing a structured taxonomy and vocabulary, resulting in a comprehensive survey that includes 33 vocabulary terms, 58 LLM prompting techniques, and 40 techniques for other modalities.
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.