Prompt Orchestration Markup Language (POML) has emerged as a new open format for crafting advanced prompts with Large Language Models (LLMs). It mirrors HTML/XML in design and offers a clear system for building repeatable, maintainable instructions for chat-based and API-driven LLMs.
It offers:
- Declarative tags such as
<role>,<task>,<example>for distinct prompt sections. - Native support for including reference files in formats like text, tables and images.
- A styling layer, modeled on CSS, that keeps formatting rules separate from core logic.
- A templating engine for variables, loops, conditionals and local definitions.
Any external content can be embedded or referenced within prompts via tags designed for specific data types:
<document>for text resources<table>for spreadsheets and tabular inputs<img>for visual aids- custom tags as required
A styling layer resembles CSS and isolates formatting rules from prompt logic. Styles can live inside <stylesheet> blocks or attach directly as inline attributes.
The built-in template engine handles {{username}} variables, for loops, if conditions and <let> definitions for modular logic.
A Visual Studio Code extension delivers syntax coloring, code completion, hover documentation, diagnostics and live previews of prompt output.
SDK packages for Node.js and Python let developers integrate POML with existing systems and mainstream LLM frameworks.
A sample POML snippet for a child-friendly lesson on photosynthesis might look like this:
<poml>
<role name="instructor"/>
<task>
Explain how plants make energy from sunlight
</task>
<document src="photosynthesis_overview.pdf"/>
<example>
<input>
What is photosynthesis in simple terms?
</input>
<output style="bullet"/>
</example>
</poml>
POML adopts a Model-View approach found in many web frameworks. Tags define structure and presentation. External code handles data access and decision logic. This split prevents tight coupling and makes it simpler to swap out components or test new variants.
The language code is available under an MIT license on GitHub alongside full documentation, examples and integration guides.
Early adopters report faster prompt iteration, fewer formatting errors and easier collaboration on multi-step workflows across teams.
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