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.

翻译：大型语言模型（LLMs）的兴起凸显了提示工程作为优化模型输出的关键技术的重要性。尽管通过少量样本提示、思维链提示和基于角色的技术等多种提示方法的实验已取得有希望的结果，但这些进展仍分散在学术论文、博客文章和轶事性实验中。缺乏一个统一的知识整合资源阻碍了研究和实际应用的进展。本文主张建立一个总体框架，将现有方法综合为面向实践者的连贯概述。采用基于设计的研究方法，我们提出了提示画布——一个通过广泛文献综述形成的结构化框架，它整合了当前提示工程领域的知识与专业经验。通过结合提示工程中识别的概念基础与实践策略，提示画布为发挥大型语言模型的潜力提供了实用方法。该框架主要设计为面向学员、学生和职员的学习资源，提供结构化的提示工程入门指导。本研究旨在通过为研究者建立统一方法论并为实践者提供指导，促进关于提示工程不断发展的学术讨论。