Document Type
Article
Publication Date
2025
DOI
10.36227/techrxiv.174140719.96375390/v1
Publication Title
TechRxiv
Pages
59 pp.
Abstract
Prompt engineering has arisen as a pivotal discipline in optimizing the performance of Large Language Models (LLMs) by structuring inputs to enhance coherence, accuracy, and task alignment. This paper comprehensively surveys various prompting techniques, systematically categorizing them according to their application domains and methodological foundations. Fundamental approaches like zero-shot and few-shot prompting are examined along with advanced strategies, including chain-of-thought reasoning, retrieval-augmented generation, and self-consistency mechanisms. A rigorous qualitative analysis is conducted to evaluate each technique's strengths, limitations, and optimal use cases, offering a structured framework for selecting the most effective prompting strategies. Theoretical insights and empirical findings are consolidated to provide researchers and practitioners with advanced methodologies for refining prompt design and enhancing LLM capabilities in complex reasoning, decision-making, and knowledge synthesis while improving reliability and factual accuracy in generated outputs.
Rights
© 2025 The Authors.
Published under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Original Publication Citation
Debnath, T., Siddiky, M. N. A., Rahman, M. E., Das, P., & Guha, A. K. (2025). A comprehensive survey of prompt engineering techniques in large language models. TechRxiv. https://doi.org/10.36227/techrxiv.174140719.96375390/v1
Repository Citation
Debnath, Tonmoy; Siddiky, Md Nurul Absar; Rahman, Muhammad Enayetur; Das, Prosenjit; and Guha, Antu Kumar, "A Comprehensive Survey of Prompt Engineering Techniques in Large Language Models" (2025). Electrical & Computer Engineering Faculty Publications. 514.
https://digitalcommons.odu.edu/ece_fac_pubs/514
ORCID
0009-0004-0421-5540 (Rahman)
Included in
Artificial Intelligence and Robotics Commons, Electrical and Computer Engineering Commons
Comments
e-Prints posted on TechRxiv are preliminary reports that are not peer reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in the media as established information.