This repository provides a comprehensive collection of tutorials and practical implementations for prompt engineering techniques, targeting AI enthusiasts, developers, and researchers. It aims to demystify prompt engineering, enabling users to effectively interact with and leverage large language models (LLMs) for diverse AI applications.

How It Works

The project offers 22 Jupyter Notebooks detailing prompt engineering methodologies, from fundamental concepts like zero-shot and few-shot learning to advanced strategies such as Chain of Thought (CoT), self-consistency, and task decomposition. Implementations primarily utilize OpenAI's GPT models and the LangChain framework, demonstrating practical application and comparative analysis of different prompt structures and techniques.

Quick Start & Requirements

• Install/Run: Clone the repository (git clone https://github.com/NirDiamant/Prompt_Engineering.git) and navigate to specific technique notebooks.
• Prerequisites: Python, OpenAI API key, LangChain.
• Resources: Requires standard development environment; notebook execution depends on LLM API usage.
• Docs: https://github.com/NirDiamant/Prompt_Engineering

Highlighted Details

• Covers 22 distinct prompt engineering techniques, categorized from fundamental to advanced.
• Includes practical implementations using Python, OpenAI API, and LangChain.
• Features explanations on prompt optimization, handling ambiguity, and prompt security.
• Addresses specialized applications like multilingual prompting and ethical considerations.

Maintenance & Community

The project encourages community contributions via pull requests and has an active Discord community for discussions and collaboration. Users can connect with the author on LinkedIn for knowledge-sharing opportunities.

Licensing & Compatibility

Licensed under a custom non-commercial license. This restricts commercial use and linking within proprietary, closed-source applications.

Limitations & Caveats

The repository is subject to a non-commercial license, limiting its use in commercial products. While comprehensive, advanced techniques may require significant computational resources and API costs for practical experimentation.