Python library for prompt engineering research
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This library provides a framework for quickly implementing and evaluating 58 different prompting techniques for large language models. It is designed for researchers and developers working with LLMs who need to systematically test and compare various prompting strategies to improve model performance.
How It Works
The library allows users to construct prompts by defining directives, additional information, and output formatting. It then enables the selection and application of specific prompting techniques, such as "System2Attention" for context clarification or "Tabular Chain of Thought" for step-by-step reasoning in math problems. A key feature is its ability to search and utilize only relevant few-shot examples for a given query, optimizing the prompt's effectiveness.
Quick Start & Requirements
pip install quality-prompts
Highlighted Details
Maintenance & Community
No information on contributors, community channels, or roadmap is provided in the README.
Licensing & Compatibility
The license is not specified in the README.
Limitations & Caveats
The README does not detail specific limitations, known bugs, or compatibility issues. The project appears to be in active development with upcoming features for evaluating techniques.
1 year ago
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