A comprehensive taxonomy of prompt engineering techniques for large language models

作者：Yaoyang Liu, Zhen Zheng, Feng Zhang, Jin-Cheng Feng, Y.H. Fu, Jidong Zhai, Bingsheng He, Xiao Zhang, Xiaoyong Du · 发表于：Frontiers of Computer Science · 年份：2025 · DOI：10.1007/s11704-025-50058-z · 被引用次数：22 · 研究领域：Multimodal Machine Learning Applications、Topic Modeling、Generative Adversarial Networks and Image Synthesis

Abstract Large Language Models (LLMs) have demonstrated remarkable performance across various downstream tasks, as evidenced by numerous studies. Since 2022, generative AI has shown significant potential in diverse application domains, including gaming, film and television, media, and finance. By 2023, the global AI-generated content (AIGC) industry had attracted over $26 billion in investment. As LLMs become increasingly prevalent, prompt engineering has emerged as a key research area to enhance user-AI interactions and improve LLM performance. The prompt, which serves as the input instruction for the LLM, is closely linked to the model’s responses. Prompt engineering refines the content and structure of prompts, thereby enhancing the performance of LLMs without changing the underlying model parameters. Despite significant advancements in prompt engineering, a comprehensive and systematic summary of existing techniques and their practical applications remains absent. To fill this gap, we investigate existing techniques and applications of prompt engineering. We conduct a thorough review and propose a novel taxonomy that provides a foundational framework for prompt construction. This taxonomy categorizes prompt engineering into four distinct aspects: profile and instruction, knowledge, reasoning and planning, and reliability. By providing a structured framework for understanding its various dimensions, we aim to facilitate the systematic design of prompts. Furthermore, we sum...