Prompt Poet is a Python library designed to simplify and enhance prompt engineering for AI models, catering to both developers and non-technical users. It leverages a low-code approach using YAML and Jinja2 templating to create flexible, dynamic, and reusable prompts, thereby improving efficiency and the quality of AI interactions.

How It Works

Prompt Poet combines YAML for structured prompt components and Jinja2 for dynamic rendering. Templates are processed in two stages: Jinja2 handles data binding, validation, and function calls, producing a structured YAML output. This YAML defines prompt "parts" with attributes like name, content, role, and truncation priority, enabling sophisticated context management and efficient LLM interaction.

Quick Start & Requirements

• Install via pip: pip install prompt-poet
• Requires Python.
• Example usage involves importing Prompt and integrating with LLM libraries like LangChain.
• Official documentation and examples are available within the README.

Highlighted Details

• Jinja2/YAML Templating: Offers expressive and extensible prompt creation with data binding, control flow, and function calls.
• Cache-Aware Truncation: Implements a custom truncation algorithm to maximize LLM GPU prefix cache hit rates for lower latency.
• Template Registry: Supports storing and loading templates from disk, with potential for in-memory caching.
• Template-Native Function Calling: Allows arbitrary Python functions to be called directly within templates for dynamic data processing.

Maintenance & Community

• The project appears to be actively maintained by character-ai.
• Links to community resources like Discord/Slack are not explicitly provided in the README.

Licensing & Compatibility

• The README does not explicitly state a license. This is a critical omission for evaluating commercial use or closed-source integration.

Limitations & Caveats

• The absence of a specified license is a significant blocker for adoption.
• While it supports custom tokenizers, the default is TikToken's "o200k_base".
• The "low-code" aspect relies on familiarity with Jinja2 and YAML syntax.