Prompt Engineering Guide
A comprehensive open-source guide covering prompt engineering techniques, papers, notebooks, and resources for LLMs, RAG, and AI agents.
What is Prompt Engineering Guide?
The Prompt Engineering Guide is an open-source educational resource that provides comprehensive materials on prompt engineering, context engineering, RAG, and AI agents for large language models. It helps users understand LLM capabilities and limitations while offering practical techniques to optimize prompts for various applications and research tasks. The guide includes papers, lessons, notebooks, and tools to support both learning and implementation.
AI researchers, machine learning engineers, developers building LLM applications, and anyone seeking structured knowledge on effective prompt design and advanced LLM techniques.
It offers a centralized, community-driven collection of up-to-date prompt engineering resources that are freely accessible, covering both foundational concepts and cutting-edge techniques. Unlike fragmented tutorials, it provides a holistic view with model-specific guidance, practical applications, and risk awareness.
Overview
🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
Use Cases
Best For
- Learning prompt engineering fundamentals and advanced techniques
- Implementing Retrieval-Augmented Generation (RAG) systems
- Developing AI agents with tool-use and reasoning capabilities
- Optimizing prompts for specific LLMs like GPT-4 or LLaMA
- Researching LLM applications in coding, data generation, or classification
- Finding curated papers, notebooks, and datasets for LLM projects
Not Ideal For
- Teams needing interactive, real-time prompt testing environments with instant feedback
- Developers looking for a software library or API to automate prompt optimization in production
- Projects focused exclusively on fine-tuning models rather than crafting prompts
- Organizations requiring certified training programs with direct vendor support
Pros & Cons
Pros
Covers everything from basic zero-shot prompting to advanced methods like Tree of Thoughts and ReAct, as detailed in the Techniques section of the guide.
Includes real-world case studies and guides for tasks like function calling and code generation, helping users apply techniques directly to projects.
Offers tailored strategies for various LLMs including ChatGPT, GPT-4, and LLaMA, ensuring optimized prompts for different model capabilities.
Addresses critical issues like adversarial prompting and biases, promoting safe and responsible LLM usage as highlighted in the Risks section.
Cons
Primarily provides text-based guides and papers without integrated interactive exercises or sandbox environments for hands-on practice.
The README heavily advertises paid courses and services like DAIR.AI Academy, which can distract from the free, open-source educational content.
Setting up the guide locally requires Node.js, pnpm, and multiple dependencies, posing a barrier for users seeking quick access.
Frequently Asked Questions
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