Prompt_Engineering and promptr
These are complements: a comprehensive reference guide (A) paired with an interactive hands-on learning platform (B) that together cover both theoretical knowledge and practical skill-building for prompt engineering techniques.
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 promptr
duplixx/promptr
Master the art of crafting perfect prompts for AI models! This interactive learning path covers beginner to advanced techniques, hands-on labs, and fun exercises to help you communicate better with AI. Contribute to the future of prompt engineering!
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