WorkDifferentWithAI.com Academic Paper Alert!
Written by Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, Douglas C. Schmidt
Category: “AI for IT”
Article Section: AI Development and Operations; AI-Assisted Programming
Publication Date: 2023-02-21
SEO Description: “Exploring Prompt Engineering with ChatGPT through Prompt Pattern Catalog for effective LLM Conversations.”
AI-Generated Paper Summary
ChatGPT-4
The article “A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT” by Jules White et. al. discusses the burgeoning field of prompt engineering, which is crucial for efficient interaction with large language models (LLMs) such as ChatGPT. It describes prompts as a form of programming used to dictate rules, automate tasks, and control the quality and quantity of output. The paper presents a catalog of successful prompt engineering techniques and discusses their analogy with software patterns, providing reusable solutions to common problems in a specific context. The key contributions of the paper include documenting patterns for structuring prompts adaptable to varied domains, illustrating the benefits of combining prompt patterns, and offering a catalog of prompt patterns that enhance the output of LLM conversations. Ultimately, the research aims to significantly improve automated software development tasks using LLMs.
Claude.ai
Based on my review of the paper, here are some key points about its novelty and potential commercial applications:
Novelty:
- Introduces the concept of “prompt patterns” as a way to document and share effective techniques for structuring prompts when interacting with large language models (LLMs) like ChatGPT. This is analogous to software design patterns.
- Provides a framework and standardized structure for documenting prompt patterns, including intent, motivation, key ideas, examples, and consequences.
- Presents a catalog of 16 initial prompt patterns, categorized into areas like output customization, error identification, interaction, etc. This provides a starting point for a shared vocabulary around prompt engineering.
- Proposes using “fundamental contextual statements” rather than formal grammars to describe the key ideas conveyed in a prompt pattern. Aims to make patterns accessible to diverse audiences.
- Demonstrates combining multiple prompt patterns to create more complex capabilities, like generating games or visualizations.
Potential Commercial Applications:
- Prompt pattern catalog could enable companies to more quickly build prompts that customize LLMs like ChatGPT for their domains and tasks. Reduces duplicate work.
- Consulting firms could offer prompt engineering services using established prompt patterns as building blocks.
- Prompt authoring tools could incorporate libraries of prompt patterns to make it easier for non-experts to create effective prompts.
- Companies could develop proprietary catalogs of prompt patterns as a form of competitive advantage in leveraging LLMs.
- Vertical SaaS companies could use prompt patterns to rapidly tailor LLMs like ChatGPT for niche applications.
Overall, the paper introduces a structured approach to prompt engineering that could accelerate and improve the application of LLMs commercially. The prompt pattern concept provides a foundation to make prompt design more systematic.
Keywords
Prompt Pattern Catalog, Enhance Prompt Engineering, ChatGPT, Large Language Models, Software Development Tasks
Author’s Abstract
Prompt engineering is an increasingly important skill set needed to converse effectively with large language models (LLMs), such as ChatGPT. Prompts are instructions given to an LLM to enforce rules, automate processes, and ensure specific qualities (and quantities) of generated output. Prompts are also a form of programming that can customize the outputs and interactions with an LLM. This paper describes a catalog of prompt engineering techniques presented in pattern form that have been applied to solve common problems when conversing with LLMs. Prompt patterns are a knowledge transfer method analogous to software patterns since they provide reusable solutions to common problems faced in a particular context, i.e., output generation and interaction when working with LLMs. This paper provides the following contributions to research on prompt engineering that apply LLMs to automate software development tasks. First, it provides a framework for documenting patterns for structuring prompts to solve a range of problems so that they can be adapted to different domains. Second, it presents a catalog of patterns that have been applied successfully to improve the outputs of LLM conversations. Third, it explains how prompts can be built from multiple patterns and illustrates prompt patterns that benefit from combination with other prompt patterns.