Awesome-Prompt-Engineering and Prompt_Engineering
These are complementary resources that together provide both curated collections of prompting strategies (A) and hands-on tutorials with implementations (B), allowing learners to discover techniques and then understand how to apply them in practice.
About Awesome-Prompt-Engineering
promptslab/Awesome-Prompt-Engineering
This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc
Organized by research area (reasoning, in-context learning, multimodal, agent systems) and practical tools (prompt management, LLM evaluation, agent frameworks), this collection spans 1,500+ papers with a taxonomy of 58+ prompting techniques alongside benchmarks, open-source implementations, and provider documentation. It bridges theory and practice by pairing foundational research with evaluation frameworks, red-teaming resources, and code repositories for prompt optimization, compression, and security testing.
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.
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