# The Prompt Report: A Systematic Survey of Prompting Techniques

**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  
**arXiv:** [2406.06608](https://arxiv.org/abs/2406.06608)  
**Date:** 2024-06-06  
**Summary page:** https://www.summarizepaper.com/en/paper/2406.06608v1/

## Key points

- Generative Artificial Intelligence (GenAI) systems are increasingly used in various industries and research settings
- Developers and end users interact through prompting or prompt engineering
- Lack of consensus on terminology and understanding of prompts due to the field's early stage
- Paper aims to establish a taxonomy of prompting techniques and analyze their usage
- Comprehensive vocabulary consisting of 33 terms, taxonomy with 58 text-only prompting techniques, and 40 techniques for other modalities
- Meta-analysis of natural language prefix-prompting literature conducted
- Various text-based techniques explored: In-Context Learning (ICL), Zero-Shot, Thought Generation, Decomposition, Ensembling
- Prompt engineering and answer engineering strategies discussed for system performance optimization
- Multilingual approaches like Chain-of-Thought (CoT), In-Context Example Selection, Prompt Template Language Selection, etc., included beyond English text prompting
- Investigation into multimodal prompting techniques such as Image Prompting, Audio Prompting, Video Prompting for enhancing GenAI systems' capabilities
- Extensions of prompting discussed including Tool Use Agents and Code-Generation Agents
- Evaluation methods for prompts explored along with security considerations like prompt hacking risks and mitigation measures
- Alignment challenges related to prompt sensitivity, biases, stereotypes addressed
- Benchmarking methodologies for comparing different prompting techniques discussed
- Case studies on prompt engineering processes provided
- Future directions for research in GenAI systems' development through effective prompt utilization strategies highlighted.

## Summary

Generative Artificial Intelligence (GenAI) systems are increasingly utilized in various industries and research settings, with developers and end users interacting through prompting or prompt engineering. While prompting is a well-researched concept, there is a lack of consensus on terminology and understanding of prompts due to the field's early stage. This paper aims to provide a structured understanding of prompts by establishing a taxonomy of prompting techniques and analyzing their usage. The study presents a comprehensive vocabulary consisting of 33 terms, a taxonomy encompassing 58 text-only prompting techniques, and an additional 40 techniques for other modalities. Furthermore, a meta-analysis of natural language prefix-prompting literature is conducted to enhance the understanding of prompt utilization. The analysis delves into various text-based techniques such as In-Context Learning (ICL), Zero-Shot, Thought Generation, Decomposition, and Ensembling. It also explores prompt engineering and answer engineering strategies to optimize system performance. Additionally, the study extends beyond English text prompting to include multilingual approaches like Chain-of-Thought (CoT), In-Context Example Selection, Prompt Template Language Selection, Prompting for Machine Translation, Segmentation Prompting, and 3D Prompting. Moreover, the research investigates multimodal prompting techniques including Image Prompting, Audio Prompting, Video Prompting, and their applications in enhancing GenAI systems' capabilities. The study also discusses extensions of prompting such as Agents like Tool Use Agents and Code-Generation Agents. Prompt evaluation methods are explored along with considerations for security issues like prompt hacking risks and mitigation measures. Alignment challenges related to prompt sensitivity, overconfidence calibration biases stereotypes culture ambiguity are also addressed. Benchmarking methodologies for comparing different prompting techniques are discussed alongside case studies on prompt engineering processes. The paper concludes by summarizing related work in the field and providing insights into future directions for research in GenAI systems' development through effective prompt utilization strategies.