# The Prompt Report: A Systematic Survey of Prompt Engineering Techniques

Source: [The Prompt Report: A Systematic Survey of Prompt Engineering Techniques](<https://www.sota2.com/research/paper/68fa771e1e209131cea7128d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:10:55.533Z

Authors: 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

Published: 2024-06-06T18:10:11.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Utility / Task Performance on Professional Domains Corpus](<https://www.sota2.com/research/sota/utility-task-performance-on-professional-domains-corpus.md>): Utility / Task Performance; Professional Domains Corpus; Utility: 91.

- [Prompt Injection Security Evaluation on Professional Domains Corpus](<https://www.sota2.com/research/sota/prompt-injection-security-evaluation-on-professional-domains-corpus.md>): Prompt Injection Security Evaluation; Professional Domains Corpus; Attack Success Rate (DevOps): 28.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2406.06608>)

[PDF](<https://arxiv.org/pdf/2406.06608.pdf>)