In this paper, we conduct a comprehensive SWOT analysis of prompt engineering techniques within the realm of Large Language Models (LLMs). Emphasizing linguistic principles, we examine various techniques to identify their strengths, weaknesses, opportunities, and threats. Our findings provide insights into enhancing AI interactions and improving language model comprehension of human prompts. The analysis covers techniques including template-based approaches and fine-tuning, addressing the problems and challenges associated with each. The conclusion offers future research directions aimed at advancing the effectiveness of prompt engineering in optimizing human-machine communication.
@article{arxiv.2410.12843,
title = {Exploring Prompt Engineering: A Systematic Review with SWOT Analysis},
author = {Aditi Singh and Abul Ehtesham and Gaurav Kumar Gupta and Nikhil Kumar Chatta and Saket Kumar and Tala Talaei Khoei},
journal= {arXiv preprint arXiv:2410.12843},
year = {2024}
}