整合现有提示工程方法，打造实用指南工具。
The Prompt Canvas: A Literature-Based Practitioner Guide for Creating Effective Prompts in Large Language Models
- 基于文献综述构建结构化提示框架
- 融合少样本、思维链等主流策略
- 适合初学者和实际应用者快速上手
大型语言模型（LLMs）的兴起凸显了提示工程在优化模型输出中的关键作用。尽管少样本、思维链和角色设定等提示方法已取得显著成效，但这些进展分散于学术论文、博客和经验分享中，缺乏统一资源阻碍了研究与实践的发展。本文提出建立一个综合框架，以整合现有方法并形成连贯的知识体系。通过设计型研究方法，我们构建了‘Prompt Canvas’——一个基于广泛文献综述的结构化框架，汇集了提示工程的理论基础与实用策略。该框架旨在为学生、员工等初学者提供系统性的学习路径，助力其有效利用大模型潜力。本研究旨在推动提示工程领域的讨论，为研究者提供统一方法论，为从业者提供实用指导。
原文摘要 · Abstract (English)
The rise of large language models (LLMs) has highlighted the importance of prompt engineering as a crucial technique for optimizing model outputs. While experimentation with various prompting methods, such as Few-shot, Chain-of-Thought, and role-based techniques, has yielded promising results, these advancements remain fragmented across academic papers, blog posts and anecdotal experimentation. The lack of a single, unified resource to consolidate the field's knowledge impedes the progress of both research and practical application. This paper argues for the creation of an overarching framework that synthesizes existing methodologies into a cohesive overview for practitioners. Using a design-based research approach, we present the Prompt Canvas, a structured framework resulting from an extensive literature review on prompt engineering that captures current knowledge and expertise. By combining the conceptual foundations and practical strategies identified in prompt engineering, the Prompt Canvas provides a practical approach for leveraging the potential of Large Language Models. It is primarily designed as a learning resource for pupils, students and employees, offering a structured introduction to prompt engineering. This work aims to contribute to the growing discourse on prompt engineering by establishing a unified methodology for researchers and providing guidance for practitioners.
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