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The Prompt Report: A Systematic Survey of Prompt Engineering Techniques

Sander Schulhoff, Michael Ilie, Nishant Balepur, Konstantine Kahadze, Amanda Liu +26 morePublished Jun 6, 2024
DOI Publisher
Researcher verdict
Context only
Use as context only
Benchmark evidence
Missing
Not verified yet
Time to first repro
A few days
Plan setup time
Risk flags
2
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Abstract

Domain fit: AI-adjacent · Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.

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.

Results and benchmarks

Freshness tier: cold
Generative Artificial Intelligence (GenAI) systems are increasingly being deployed across diverse industries and research domains.

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Last checked: Aug 24, 2026

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Research context

94

Citations

0

References

Tasks

Psychology, Medicine, Health Informatics, Health Sciences

Methods

Transformer

Domains

None detected

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