The Prompt Canvas: A Literature-Based Practitioner Guide for Creating Effective Prompts in Large Language Models
This work provides a unified resource for practitioners and researchers in prompt engineering, though it is incremental as it consolidates existing knowledge rather than introducing new methods.
The paper addresses the fragmentation of prompt engineering knowledge by introducing the Prompt Canvas, a structured framework synthesized from an extensive literature review to provide a cohesive guide for practitioners, aiming to unify methodologies and offer practical strategies for leveraging large language models.
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.