PromptML (Prompt Markup Language)
Prompt Markup Language a.k.a PromptML is a programming language designed to write AI prompts as code.
Why PromptML ?
PromptML provides a way for prompt engineers to define the AI prompts in a deterministic way. This is a Domain Specific Language (DSL) which defines characteristics of a prompt including context, objective, instructions and it's metadata. A regular prompt is an amalgamation of all these aspects into one natural language sentence. PromptML splits it into multiple sections and makes the information explicit.
The language grammar can be found here: grammar.lark
Installing promptml
To get the latest version of promptml parser, run the following pip command:
pip install promptml
How PromptML looks ?
The language is simple. You start blocks with @
section annotation. A section ends with @end
marker. Comments are started with #
key. The prompt files ends with .pml
extension.
@prompt
# Add task context
@context
@end
# Add task objective
@objective
# This is the final question or ask
@end
# Add one or more instructions to execute the prompt
@instructions
@step
@end
@end
# Add one or more examples
@examples
@example
@input
# Add your example input
@end
@output
# Add your example output
@end
@end
@end
# Add task constraints
@constraints
@length min: 1 max: 10 @end
@end
# Add prompt category
@category
@end
# Add custom metadata
@metadata
@end
@end
See prompt.pml to see for complete syntax.
Design
Regular text prompts are very abstract in nature. Natural languages are very flexible but provides least reliability. How to provide context for an AI system and ask something ? Shouldn't we specify that explicitly. PromptML is an attempt to make contents of a prompt explicit with a simple language.
Core tenets of PromptML
Below are the qualities PromptML brings to prompt engineering domain:
- Standardization instead of fragmentation
- Collaboration instead of confusion
- Enabling version control-ability
- Promoting verbosity for better results
Why not use XML, YAML, or JSON for prompt engineering ?
First, XML, JSON, and YAML are not DSL languages. They are data formats that can represent any form of data. Second, generative AI needs a strict, yet flexible data language with fixed constraints which evolve along with the domain.
PromptML is built exactly to solve those two issues.
Language grammar is influenced by XML & Ruby, so if you know any one of them, you will feel very comfortable writing prompts in PromptML.
Usage
- Install Python requirements
pip install -r requirements.txt
- import the parser and parse a promptML file
from promptml.parser import PromptParser
promptml_code = '''
@prompt
@context
This is the context section.
@end
@objective
This is the objective section.
@end
@instructions
@step
Step 1
@end
@end
@examples
@example
@input
Input example 1
@end
@output
Output example 1
@end
@end
@end
@category
Prompt Management
@end
@constraints
@length min: 1 max: 10 @end
@end
@metadata
top_p: 0.9
n: 1
team: promptml
@end
@end
'''
parser = PromptParser(promptml_code)
prompt = parser.parse()
print(prompt)
# Output: {
# 'context': 'This is the context section.',
# 'objective': 'This is the objective section.',
# 'category': 'Prompt Management',
# 'instructions': ['Step 1'],
# 'examples': [
# {'input': 'Input example 1', 'output': 'Output example 1'}
# ],
# 'constraints': {'length': {'min': 1, 'max': 10}},
# 'metadata': {'top_p': 0.9, 'n': 1, 'team': 'promptml'}
# }
Defining variables
You can define variables in the promptML file and use them in the prompt context
and objective
. The variables are defined in the @vars
section and referenced using $var
syntax in either context
or objective
sections.
@vars
name = "John Doe"
@end
@prompt
@context
You are a name changing expert.
@end
@objective
You have to change the name: $name to an ancient name.
@end
@end
TODO
We are currently working on:
VSCode
syntax highlighting support- Add more unit tests
- Add support for
XML
&YAML
serialization