Microsoft Pummel: The Revolutionary Markup Language for Writing Perfect AI Prompts

As AI becomes increasingly central to development workflows, the challenge of writing effective prompts has emerged as a critical skill. Microsoft's answer to this challenge is Pummel - an open-source markup language specifically designed to help developers write reliable and maintainable AI prompts. This powerful tool transforms the way we interact with Large Language Models by adding structure, reusability, and advanced features to what would otherwise be plain text prompts.

What Is Pummel and Why Do You Need It?

Pummel is an HTML-like markup language developed by prompt engineering researchers at Microsoft. While you might initially wonder why you'd need a specialized language just to write prompts, Pummel's capabilities quickly demonstrate its value - particularly for complex AI interactions or when working with prompts at scale.

The language allows you to compile prompts into various formats (markdown, JSON, HTML, YAML, or XML) and offers powerful features like external file integration, web content extraction, and templating. These capabilities make it especially valuable for prompt engineering professionals working with Azure OpenAI services or other LLM platforms.

Getting Started with Pummel

While Pummel does have a TypeScript SDK, it's not yet fully stable. For now, the Python SDK provides the most reliable experience. Here's a simple example of how to use it:

You can also load Pummel files directly:

Understanding Pummel Components

Pummel organizes content into "components" - specialized tags that help structure your prompts. These components fall into several categories:

• Basic components: , ,
• Intentional components: (role), , , ,
• Data components: , ,

  

  A simple Pummel file might look like this:

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  <pummel>
    <ro>You are a helpful assistant</ro>
    <task>Write a short poem about artificial intelligence</task>
    <hint>Make it optimistic about the future</hint>
  </pummel>

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  Integrating External Data

  One of Pummel's most powerful features is its ability to seamlessly incorporate external data into prompts. This capability is particularly valuable for prompt engineering in Microsoft's Azure AI ecosystem, where working with structured data is common.

  For example, you can include data from Excel files, Word documents, or even specific sections of websites:

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  <pummel>
    <task>Analyze this data and provide insights</task>
    <document src="data.xlsx" limit="5" convert="csv" />
  </pummel>

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  This will automatically extract the first 5 records from the Excel file, convert them to CSV format, and include them in your prompt - regardless of whether the final output is in markdown, JSON, or another format.

  

  Advanced Templating with Pummel

  Pummel includes a powerful templating engine that enables dynamic prompt generation. You can create variables, use conditional logic, and even loop through data:

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  <pummel>
    <let name="language">Python</let>
    <task>Write a {{language}} function that calculates factorial</task>
    
    <if condition="language == 'Python'">
      <hint>Use a recursive approach</hint>
    </if>
  </pummel>

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  You can also pass data from your Python code into Pummel templates using the context parameter:

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  from pummel import Pummel
  
  context = {
    "user_name": "Alice",
    "people": [{"name": "Bob", "role": "Developer"}, {"name": "Charlie", "role": "Designer"}]
  }
  
  prompt = Pummel.from_file("template.pml", context=context)
  output = prompt.to_markdown()

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  Practical Use Case: Generating an Agent.md File

  To demonstrate Pummel's practical value for AI prompt engineering, let's look at a real-world example: automatically generating an agent.md file for a development project based on coding style preferences.

  

  The process involves:

  1. Creating a Pummel file that defines the structure of the prompt
  2. Using conditional logic to include different code style documents based on parameters
  3. Passing the compiled prompt to an LLM (in this case, GPT-5 nano)
  4. Saving the generated content as an agent.md file
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  from pummel import Pummel
  import openai
  import argparse
  import os
  
  # Parse command line arguments
  parser = argparse.ArgumentParser()
  parser.add_argument("--react", action="store_true")
  parser.add_argument("--typescript", action="store_true")
  args = parser.parse_args()
  
  # Create context for Pummel template
  context = {
      "use_react": args.react,
      "use_typescript": args.typescript
  }
  
  # Load and compile the Pummel template
  prompt = Pummel.from_file("agent_template.pml", context=context)
  formatted_prompt = prompt.to_openai_chat()
  
  # Send to OpenAI
  response = openai.chat.completions.create(
      model="gpt-5-nano",
      messages=formatted_prompt
  )
  
  # Save the response as agent.md
  with open("agent.md", "w") as f:
      f.write(response.choices[0].message.content)
  
  print("Created agent.md file")

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  Additional Pummel Features

  Pummel offers several other advanced capabilities that make it a comprehensive solution for prompt engineering in Microsoft's AI ecosystem:

  • Stylesheets for specifying data formats
  • Tool specification for LLM function calling
  • Expression evaluation using ZOD
  • Trace capture for debugging prompts before sending to LLMs

  Integrating with OpenAI and Other LLM Providers

  One of Pummel's strengths is its ability to output prompts in formats compatible with various LLM providers. For OpenAI integration, you can use the to_openai_chat() method:

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  from pummel import Pummel
  import openai
  
  prompt = Pummel.from_file("my_prompt.pml")
  formatted_prompt = prompt.to_openai_chat()
  
  response = openai.chat.completions.create(
      model="gpt-4",
      messages=formatted_prompt
  )
  
  print(response.choices[0].message.content)

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  This approach works seamlessly with Microsoft's Azure OpenAI services as well, making Pummel an excellent choice for prompt engineering in Microsoft-centric AI development environments.

  Conclusion

  Pummel represents a significant advancement in prompt engineering, particularly for complex AI applications. By providing structure, reusability, and advanced data integration capabilities, it transforms prompt creation from an ad-hoc process into a systematic engineering discipline.

  For developers working with Microsoft's AI services like Azure OpenAI, Pummel offers a natural extension to existing workflows, enabling more sophisticated and maintainable prompt design. While it may seem complex at first glance, the benefits become apparent when dealing with complex prompts or when working with prompts at scale.

  As AI continues to evolve as a core development tool, structured approaches to prompt engineering like Pummel will likely become increasingly important, especially in enterprise environments where consistency and maintainability are paramount.

  Let's Watch!

  Microsoft Pummel: The Secret Tool for Writing Perfect AI Prompts

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