Prompt_Engineering and Prompt-Engineering-Guide-zh-CN
These are complements—the English-language comprehensive tutorial collection and Chinese-language guide serve different linguistic audiences learning the same prompt engineering discipline, making them mutually reinforcing resources rather than substitutes.
About Prompt_Engineering
NirDiamant/Prompt_Engineering
This repository offers a comprehensive collection of tutorials and implementations for Prompt Engineering techniques, ranging from fundamental concepts to advanced strategies. It serves as an essential resource for mastering the art of effectively communicating with and leveraging large language models in AI applications.
Organized into 22 Jupyter Notebook tutorials, the repository covers techniques across foundational concepts (prompt structures, templating with Jinja2), core methods (zero-shot, few-shot, chain-of-thought), and advanced strategies. Implementations use major LLM APIs (OpenAI, Anthropic, Cohere) with practical code examples demonstrating each technique in action. The project emphasizes hands-on experimentation through executable notebooks while fostering community contributions via Discord and GitHub, complementing related repositories on RAG and production-grade AI agents.
About Prompt-Engineering-Guide-zh-CN
yunwei37/Prompt-Engineering-Guide-zh-CN
🐙 关于提示词工程（prompt）的指南、论文、讲座、笔记本和资源大全（自动持续更新）
Organizes prompt engineering knowledge across structured guides covering foundational techniques, advanced strategies, adversarial robustness, and LLM-specific applications like ChatGPT, complemented by curated research papers, tools, and datasets. The collection includes interactive Jupyter notebooks with executable code examples, video lectures, and slides demonstrating practical prompt optimization techniques for improving LLM performance on reasoning and question-answering tasks. Maintained with automated updates to track the rapidly evolving landscape of prompt engineering research and best practices.
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