Prompt Orchestration Markup Language

Summary

This paper introduces POML, a new language designed to make it easier to create and manage complex instructions, or "prompts," for AI models like Large Language Models. It focuses on organizing different types of information, handling how things look, and making it simpler for developers to work with these prompts.

What's the problem?

Creating good prompts for AI models can be really tricky. It's hard to organize them when you have lots of different kinds of information, like text, tables, or even pictures. Also, making sure the prompt looks right and works consistently when you change how it's presented is a big headache. Current ways of doing this aren't very organized or flexible.

What's the solution?

The paper proposes POML, which uses a system similar to how websites are built with HTML and CSS. It breaks prompts into organized pieces, has special ways to include different kinds of data smoothly, and lets you control how things look separately from the content. This makes prompts more adaptable and less likely to break if you change formatting. It also includes tools to help developers manage and share their prompts more effectively.

Why it matters?

This is important because as AI models get more powerful, how we communicate with them through prompts becomes crucial for getting the best results. POML aims to make this communication process much more efficient and reliable, especially for complex tasks involving diverse data, which will help developers build better AI-powered applications more easily.