Automatic Prompt Engineering Technology for Large Language Models: a Survey

doi:10.3778/j.issn.1673-9418.2502027

Abstract

Abstract: Prompt engineering (PE) based on prompt learning is crucial for improving the technical accessibility of LLMs and accelerating their adoption, diffusion, and application development. Compared with traditional PE, which heavily relies on the domain knowledge and experience of prompt designers and is less adaptable to tasks with large prompt spaces, automatic prompt engineering (APE) can generate or optimize prompts in an automatic or semi-automatic way. This enables the exploration of large-scale prompt combinations and enhances the stability of prompt generation through automated optimization techniques. However, there is currently a lack of systematic reviews on APE, which hinders subsequent researchers from quickly grasping the state of the field. Therefore, this paper keeps up with the latest research developments, systematically reviews the implementation forms of automated prompt engineering, and proposes future research directions. Based on the trade-offs in logical reasoning and performance orientation in the implementation of a APE, this paper categorizes it into four main types: APE based on chain-of-thought, APE based on machine learning models, APE based on evolutionary algorithms and plug-and-play auto-prompt systems. Subsequently, this paper conducts a comprehensive evaluation of APE techniques, constructing a theoretical explanatory framework for their working principles and assessing the applicability and limitations of each implementation form. Finally, this paper looks ahead to the development trends of APE in multimodal large models, advanced reasoning models and AI-Agents.

Key words: large language models, prompt engineering, automatic prompt engineering, chain of thought, machine learning, evolutionary algorithms, plug-and-play systems

摘要： 基于提示学习的提示工程对于提升大语言模型的技术可及性、加速其推广扩散与应用开发至关重要。传统的提示工程过度依赖于提示词设计者的领域知识和使用经验，且不易满足提示空间较大的任务；相比之下，自动化提示工程能够自动化或半自动化地生成或优化提示词，以探索大规模的提示词组合，并通过自动优化技术提升提示词生成的稳定性。然而目前仍缺乏对自动化提示研究的系统性综述，因此，及时跟进该领域的最新研究成果，详细梳理并评述自动化提示工程的实现形式，提出自动化提示工程的未来研究方向。依据自动化提示工程实现形式在逻辑推理和效能导向两个维度的取舍上，将其分为基于思维链的自动化提示工程、基于类机器学习模型的自动化提示工程、基于进化算法的自动化提示工程以及使用预训练包的即插即用系统。全面评估自动化提示工程技术，构建其工作原理的理论解释框架，评估各类实现形式的适用性与局限性。最后，展望多模态大模型、强推理模型以及智能体中自动化提示工程的发展趋势。

关键词: 大语言模型, 提示工程, 自动化提示工程, 思维链, 机器学习, 进化算法, 即插即用系统

BA Zezhi, ZHANG Hui, XIE Zhenghan, ZUO Xiaodong, HOU Jianwei. Automatic Prompt Engineering Technology for Large Language Models: a Survey[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(12): 3131-3152.

巴泽智, 张辉, 谢铮涵, 左晓栋, 侯健玮. 大语言模型自动化提示工程技术研究综述[J]. 计算机科学与探索, 2025, 19(12): 3131-3152.

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