Go to DBLP homepageA comprehensive taxonomy of prompt engineering techniques for large language models
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 summarize existing prompt engineering techniques and explore the applications of LLMs across various domains, highlighting their interrelation with prompt engineering strategies. This survey underscores the progress of prompt engineering and its critical role in advancing AI applications, ultimately aiming to provide a systematic reference for future research and applications.
External IDs:dblp:journals/fcsc/LiuZZFFZHZD26
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