Unlocking ChatGPT's Potential: a Literature Review of Prompt Engineering Strategies Across Diverse Domains

EasyChair Preprint 12508

Abstract

Large Language Models (LLMs), exemplified by OpenAI's ChatGPT, represent a significant advancement in artificial intelligence, demonstrating human-like text generation and contextual understanding. Contrary to misconceptions, ChatGPT enhances creativity and efficiency instead of reducing human involvement. The model's efficacy relies on well-crafted prompts, emphasizing the importance of clear and context-rich communication. The literature review of seven papers underscores the crucial role of tailored prompts in optimizing ChatGPT's performance across diverse domains. This analysis reveals both commonalities and distinctions in prompt engineering strategies, with a consistent theme of specificity and structure. The adaptability of ChatGPT across various fields is evident, showcasing its potential in scientific research, entrepreneurship, and education.

Keyphrases: GPT, Prompt Engineering, analysis, program engineering

@booklet{EasyChair:12508,
  author    = {Ivan Stanislavov Ivanov and Jiyoung Song},
  title     = {Unlocking ChatGPT's Potential: a Literature Review of Prompt Engineering Strategies Across Diverse Domains},
  howpublished = {EasyChair Preprint 12508},
  year      = {EasyChair, 2024}}