Summary of The Prompt Canvas: a Literature-based Practitioner Guide For Creating Effective Prompts in Large Language Models, by Michael Hewing and Vincent Leinhos
The Prompt Canvas: A Literature-Based Practitioner Guide for Creating Effective Prompts in Large Language Models
by Michael Hewing, Vincent Leinhos
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
Summary difficulty | Written by | Summary |
---|---|---|
High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework, Prompt Canvas, is proposed to unify various prompting methods for optimizing Large Language Model (LLM) outputs. The development of this framework is essential as current knowledge remains scattered across academic papers, blog posts, and anecdotal experimentation. To achieve this unification, a design-based research approach was employed, combining conceptual foundations and practical strategies from the literature on prompt engineering. The resulting Prompt Canvas provides a structured overview of current methodologies for practitioners, serving as both a learning resource for students and employees, and a contribution to the growing discourse on prompt engineering. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) have become very powerful tools that can generate text, summarize documents, and even chat with us. To get these models to perform better, we need to give them “prompts” or hints about what kind of output we want. Right now, there are many different ways to do this, but they’re not organized in a way that makes sense. This paper proposes a new framework called the Prompt Canvas, which brings together all the different approaches into one easy-to-use system. The authors used a research design approach to develop this framework, combining ideas from lots of other papers and studies on prompt engineering. The goal is to make it easier for people to learn about and use these prompts, so they can get the most out of their LLMs. |
Keywords
» Artificial intelligence » Discourse » Large language model » Prompt » Prompting