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Cited by 490

The DOI matches its Crossref record; 262 of the 262 references it cites were found in a registry or an index.

Unleashing the potential of prompt engineering for large language models

Open access
Chen, Banghao, Zhang, Zhaofeng, Langrené, Nicolas, Zhu, Shengxin·2025·Patterns

Abstract

This review explores the role of prompt engineering in unleashing the capabilities of large language models (LLMs). Prompt engineering is the process of structuring inputs, and it has emerged as a crucial technique for maximizing the utility and accuracy of these models. Both foundational and advanced prompt engineering methodologies-including techniques such as self-consistency, chain of thought, and generated knowledge, which can significantly enhance the performance of models-are explored in this paper. Additionally, the prompt methods for vision language models (VLMs) are examined in detail. Prompt methods are evaluated with subjective and objective metrics, ensuring a robust analysis of their efficacy. Critical to this discussion is the role of prompt engineering in artificial intelligence (AI) security, particularly in terms of defending against adversarial attacks that exploit vulnerabilities in LLMs. Strategies for minimizing these risks and improving the robustness of models are thoroughly reviewed. Finally, we provide a perspective for future research and applications.

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Citations

Cites

Show all 262 references

Research works printed but not linked: 15

  1. Achiam, 2024, GPT-4 technical report, arXiv
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  15. Bertucci, 2022, Deep learning in finance: from implementation to regulation, arXiv

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Metadata

Authors
Chen, B., Zhang, Z., Langrené, N., & Zhu, S.
Venue
Patterns
Publisher
Elsevier BV
Published
2025-06
Volume
6
Issue
6
Pages
101260
ISSN
2666-3899 (ISSN-L)
OpenAlex
W4410202262
Cited by
490
References
262
FWCI
186.42
Type
journal article
Language
en
Open access
gold
Licence
Funders
  • National Natural Science Foundation of China (12271047)
  • Hong Kong Baptist University
  • Beijing Normal University

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Chen, B., Zhang, Z., Langrené, N., & Zhu, S. (2025). Unleashing the potential of prompt engineering for large language models. Patterns. https://doi.org/10.1016/j.patter.2025.101260

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