The Prompt Canvas: A Literature-Based Practitioner Guide for Creating Effective Prompts in Large Language Models
Michael Hewing, Vincent Leinhos

TL;DR
This paper introduces the Prompt Canvas, a comprehensive framework synthesized from literature to guide practitioners in creating effective prompts for large language models, aiming to unify and advance prompt engineering practices.
Contribution
It presents the Prompt Canvas, a novel structured framework derived from extensive literature review, to unify prompt engineering methodologies for researchers and practitioners.
Findings
Developed the Prompt Canvas framework based on literature synthesis
Provides a structured overview of prompt engineering techniques
Aims to enhance practical application and research in prompt design
Abstract
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…
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Taxonomy
TopicsEducational Tools and Methods · Topic Modeling · Innovative Teaching and Learning Methods
