Prompt_Engineering and Learn_Prompting
These are complementary educational resources that serve different learning styles—one providing hands-on code implementations and tutorials for prompt engineering techniques, while the other offers a structured guide and community-driven learning platform covering the same domain.
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 Learn_Prompting
trigaten/Learn_Prompting
Prompt Engineering, Generative AI, and LLM Guide by Learn Prompting | Join our discord for the largest Prompt Engineering learning community
Built as a Next.js static site, the project delivers comprehensive prompt engineering education through an open-source guide cited by OpenAI and Google, supplemented by 15 structured courses covering generative AI and LLM techniques. The repository includes research artifacts like "The Prompt Report" (systematic survey of prompting methods) and datasets from HackAPrompt (600K+ adversarial prompts), enabling community-driven contributions across translations, content, and artwork.
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