As AI large language models like ChatGPT become increasingly popular, prompts have become the most common way to interact with them, leading to extensive and in-depth research. The media is busy spreading various magical incantations. Jules White et al.'s paper A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT summarizes and analyzes various prompts, proposing many valuable patterns. This project analyzes and categorizes patterns from the perspectives of Issue Mapping and Issue-based information system - IBIS, adding more patterns and examples. Users can draw on this method to further develop their own prompting systems.
Developers are welcome to participate in this project by submitting their own pattern designs (positions), examples/counter examples (pros/cons), and discovered questions (issues). Just modify the prompt-patterns*.yaml files in the project or discuss in the forum.
It is clear that LLMs can effectively classify text data based on IBIS, and we will integrate APIs like OpenAI to develop more convenient ways for public participation. Everyone is welcome to join in the development👏
随着ChatGPT等AI大语言模型的流行，提示词（Prompt）作为最常用的使用方式得到了深入和广泛的研究，媒体忙着传播各种魔法口诀。Jules White等人的论文 A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT)对各种Prompt做了总结分析，提出了许多有价值的模式（Pattern）。本项目从Issue Mapping和Issue-based information system - IBIS的方式出发对模式进行了分析和分类，加入了更多的模式和实例。用户可以借鉴此方法继续发展自己的提示系统。
欢迎广大开发者参与本项目，提交自己设计的模式（Pattern - Position）、例子(examples/counter examples - pros/cons）和发现的问题（Question - Issue）。只需要修改项目中的prompt-patterns*.yaml文件，或者在讨论区讨论。
很显然，LLM能够很好的对文本数据进行IBIS分类，我们会接入OpenAI等API来开发更简便的大众参与方式。欢迎参与开发👏
The web app uses mxGraph.