Prompt_Engineering and DeepSeek_Prompt_Engineering
These are complements: the first provides broad prompt engineering techniques and methodologies applicable across models, while the second offers a specialized implementation guide for applying those techniques specifically with the DeepSeek API.
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 DeepSeek_Prompt_Engineering
Minghao-Liang/DeepSeek_Prompt_Engineering
A Tutorial on learning Prompt Engineering with DeepSeek API.
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