Prompt_Engineering and awesome-automated-prompt-engineering
The first repository offers tutorials and implementations for learning prompt engineering techniques, while the second serves as a hub for discovering automated prompt engineering tools, making them complements as a user might learn concepts from the first and then seek out tools from the second to apply those concepts automatically.
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 awesome-automated-prompt-engineering
The-Swarm-Corporation/awesome-automated-prompt-engineering
This repository serves as a central hub for discovering tools and services focused on automated prompt engineering. Whether you're looking to optimize your prompts for generative AI models or enhance the capabilities of your agents, you'll find a wide range of resources here.
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