The Prompt Canvas: A Literature-Based Practitioner Guide for Creating Effective Prompts in Large Language Models (2412.05127v1)

Abstract: The rise of 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 LLMs. 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.

• The paper introduces the Prompt Canvas, a comprehensive framework that consolidates scattered prompt engineering techniques through an extensive literature review.
• It details key components such as Few-shot learning, Chain-of-Thought reasoning, and iterative feedback loops to optimize LLM outputs.
• The framework offers practical guidance for educators and practitioners to enhance LLM performance across diverse applications.

The Prompt Canvas: A Consolidated Framework for Effective Prompt Engineering

The paper "The Prompt Canvas: A Literature-Based Practitioner Guide for Creating Effective Prompts in LLMs" by Michael Hewing and Vincent Leinhos addresses the segmentation and fragmentation of prompt engineering knowledge across various mediums. The authors advocate for a unified framework aimed at consolidating this disparate wisdom into a comprehensive, practitioner-oriented guide, termed the "Prompt Canvas." This framework draws on an extensive literature review, methodically categorizing the techniques and strategies documented in the scattered landscape of prompt engineering.

Overview and Structure

The authors introduce the concept of prompt engineering as an essential mediator between user objectives and LLM capabilities, steering models towards desired outcomes without necessitating task-specific retraining. Despite the recognition of prompt engineering's significance, much of the field's progress remains disjointed due to its documentation across diverse sources, including academic papers, blogs, and other informal platforms.

The paper systematically dissects this field through a design-based research approach, establishing the Prompt Canvas as a structured framework designed to aggregate current knowledge into an accessible and practical resource. The authors emphasize its utility as a learning tool for educators, students, and employees, aligning academic inquiry with practical application.

Methodology and Findings

Hewing and Leinhos underline the necessity for a coherent overview of prompting techniques due to the expansive and fragmented knowledge landscape. This paper brings together key elements such as Few-shot learning, Chain-of-Thought reasoning, and iterative feedback loops, representing foundational and nuanced prompt engineering methods. Using an exhaustive systematic literature review, they synthesize prevalent prompting techniques, organizing them into a schema conducive for both researchers and practitioners.

The Prompt Canvas itself is meticulously detailed, consisting of four categories: Persona/Role and Target Audience, Goal and Step-by-Step, Context and References, and Output/Format and Tonality. Each element represents a vital component of prompt construction, leveraged to optimize interaction and outcome in LLM engagements.

Implications and Future Directions

The paper's contribution lies in demystifying the multifaceted domain of prompt engineering, offering a consolidated reference point that mitigates the knowledge access barrier for practitioners. This has practical significance considering the breadth of LLM applications across various industries, from healthcare to software development. By unifying these fragmented insights into a cohesive framework, the Prompt Canvas aids in both the teaching and application of effective prompting techniques.

The authors invite ongoing discourse regarding the Prompt Canvas's adaptability in light of continuous advancements in AI and LLM capabilities. This research sets the stage for exploring whether new developments warrant integration into the framework, ensuring it remains relevant. The dynamic and iterative nature of prompt engineering necessitates that tools like the Prompt Canvas evolve in step with technological progressions, reflecting emergent best practices and innovative prompting techniques.

Conclusion

The Prompt Canvas positions itself as a vital tool for practitioners navigating the evolving landscape of prompt engineering. This framework stands out for its synthesis of existing knowledge into a pragmatic guide, fostering both academic rigor and practical utility. It exemplifies a structured approach to leveraging LLMs effectively, underscoring the importance of foundational and advanced prompting techniques in maximizing model potential across diverse applications.