Dotprompt: Executable GenAI Prompt Templates
Dotprompt is an executable prompt template file format for Generative AI. It is designed to be agnostic to programming language and model provider to allow for maximum flexibility in usage. Dotprompt extends the popular Handlebars templating language with GenAI-specific features.
What's an executable prompt template?
An executable prompt template is a file that contains not only the text of a prompt but also metadata and instructions for how to use that prompt with a generative AI model. Here's what makes Dotprompt files executable:
-
Metadata Inclusion: Dotprompt files include metadata about model configuration, input requirements, and expected output format. This information is typically stored in a YAML frontmatter section at the beginning of the file.
-
Self-Contained Entity: Because a Dotprompt file contains all the necessary information to execute a prompt, it can be treated as a self-contained entity. This means you can "run" a Dotprompt file directly, without needing additional configuration or setup in your code.
-
Model Configuration: The file specifies which model to use and how to configure it (e.g., temperature, max tokens).
-
Input Schema: It defines the structure of the input data expected by the prompt, allowing for validation and type-checking.
-
Output Format: The file can specify the expected format of the model's output, which can be used for parsing and validation.
-
Templating: The prompt text itself uses Handlebars syntax, allowing for dynamic content insertion based on input variables.
This combination of features makes it possible to treat a Dotprompt file as an executable unit, streamlining the process of working with AI models and ensuring consistency across different uses of the same prompt.
Example .prompt file
Here's an example of a Dotprompt file that extracts structured data from provided text:
---
model: googleai/gemini-2.5-pro
input:
schema:
text: string
output:
format: json
schema:
name?: string, the full name of the person
age?: number, the age of the person
occupation?: string, the person's occupation
---
Extract the requested information from the given text. If a piece of information is not
present, omit that field from the output. Text:
{{text}}
This Dotprompt file:
- Specifies the use of the
googleai/gemini-2.5-promodel. - Defines an input schema expecting a
textstring. - Specifies that the output should be in JSON format.
- Provides a schema for the expected output, including fields for name, age, and occupation.
- Uses Handlebars syntax (
{{text}}) to insert the input text into the prompt.
When executed, this prompt would take a text input, analyze it using the specified AI model, and return a structured JSON object with the extracted information.
Runtime context
Prompt input and runtime context are separate namespaces. Use {{name}} for
input and {{@name}} for context:
from dotpromptz import Dotprompt
from dotpromptz.typing import DataArgument
prompt = Dotprompt()
result = await prompt.render(
'{{name}} is signed in as {{@name}}',
DataArgument(
input={'name': 'Ada'},
context={'name': 'admin'},
),
)
Metadata
Release files for dotpromptz 0.1.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| dotpromptz-0.1.6.tar.gz | 90.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| dotpromptz-0.1.6-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 155.3 kB
Release files / dotpromptz-0.1.6.tar.gz
| Download URL | dotpromptz-0.1.6.tar.gz |
|---|---|
| Size | 90.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
|
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|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
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Transparency logRelease files / dotpromptz-0.1.6-py3-none-any.whl
| Download URL | dotpromptz-0.1.6-py3-none-any.whl |
|---|---|
| Size | 64.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
240dc937ff547ddc1031cbaa58fb53b5fe155929c7a34e080ef39e04e2b39248
|
|
BLAKE2b-256 checksum How to use checksums |
2e21490b3af168840e2c855031dfe7c083ee907d632819eca5e06c7cbd6ce747
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 24, 2026.
Transparency log