Prompt_Engineering and prompt-engineering
These are competitors offering overlapping educational resources on prompt engineering, with A providing a comprehensive English-language tutorial collection while B offers a Chinese-language manual approach, requiring users to choose one based on language preference and learning style rather than complementary functionality.
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
1Haschwalth/prompt-engineering
自撰作品《AI精准操作手册：从Prompt工程到认知导航》（AI Precision Operations Manual: From Prompt Engineering to Cognitive Navigation）
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