Prompt_Engineering and Prompt-Engineering-Jumpstart
These are complementary resources where one provides beginner-friendly foundational principles with practical examples while the other offers comprehensive technical depth across implementation strategies, making them ideal for sequential learning from basics to advanced techniques.
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-Jumpstart
arorarishi/Prompt-Engineering-Jumpstart
Prompt Engineering Jumpstart: The simplest beginner's guide to talking with AI. Learn universal prompt engineering principles with simple analogies & copy-paste examples for ChatGPT & DALL-E. No tech skills needed. Free, open-source eBook.
The guide covers 14 core prompting patterns—specificity, persona assignment, few-shot learning, chain-of-thought reasoning, output formatting, iteration, negative prompting, and task chaining—each with before/after examples and practice exercises. It approaches prompt engineering as universal "grammar" applicable across ChatGPT, Claude, Copilot, Gemini, and image models like DALL-E, treating it as a learnable skill rather than trial-and-error guessing. Content is structured as a complete open-source markdown eBook with chapters, visual summaries, and a dedicated cheat sheet for quick reference.
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