Promptify and prompt-poet
These are complements: Promptify provides structured output extraction and prompt versioning for production use, while Prompt Poet offers low-code prompt design and iteration for creating those prompts, making them useful in sequence during development and deployment workflows.
About Promptify
promptslab/Promptify
Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research
Provides task-specific NLP classes (NER, classification, QA, relation extraction) that return type-safe Pydantic models instead of raw text, eliminating parsing brittleness. Abstracts away LLM provider differences through LiteLLM, allowing seamless switching between OpenAI, Anthropic, Ollama, and 100+ other backends with a single model string. Includes built-in evaluation metrics (precision, recall, F1) and cost tracking, plus batch/async processing for production workloads.
About prompt-poet
character-ai/prompt-poet
Streamlines and simplifies prompt design for both developers and non-technical users with a low code approach.
Combines YAML-based prompt structure with Jinja2 templating to support conditional logic, dynamic message lists, and LangChain integration—enabling features like context-aware few-shot examples and automatic message truncation by priority. Built-in tokenization provides granular token accounting across nested prompt sections, helping optimize context length constraints. Handles complex, compositional prompts through template includes and variable interpolation while maintaining clean separation between prompt logic and data.
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