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Prompt-Engineering-Guide

Free

🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.

Search ToolsFreeFree tier
Type
Open Source
Company
DAIR.AI
Visit Website GitHub (74.1k stars)

About Prompt-Engineering-Guide

The Prompt Engineering Guide is a comprehensive resource by DAIR.AI covering prompt engineering, context engineering, RAG, and AI Agents. It includes guides, papers, lessons, notebooks, and datasets to help researchers and practitioners understand LLM capabilities, improve safety, and design robust prompting techniques. The guide covers advanced techniques such as zero-shot, few-shot, chain-of-thought, tree of thoughts, ReAct, and many others, along with model-specific prompting guides for popular models like ChatGPT, Claude, Gemini, and GPT-4. It is open source and welcomes community contributions.

Key Features

Comprehensive coverage of prompt engineering techniques including zero-shot, few-shot, chain-of-thought, tree of thoughts, RAG, and more
Model-specific prompting guides for ChatGPT, Claude, Gemini, GPT-4, Llama, and other LLMs
Collection of relevant papers, notebooks, and datasets for practical learning
Sections focused on safety, adversarial prompting, and bias mitigation
Dedicated resources for context engineering and AI Agents
Open source with community contributions and regular updates

Pros & Cons

Pros
  • Free and open source, accessible to everyone
  • Regularly updated with latest research and techniques
  • Covers a wide array of models and prompting methods in one place
  • Includes practical examples and ready-to-use notebooks
  • Community-driven with contribution opportunities
Cons
  • Primarily a reference guide, not an interactive tool or playground
  • May be overwhelming for absolute beginners due to depth and breadth
  • Some sections assume prior knowledge of LLMs and prompting fundamentals

Best For

Educating researchers and practitioners in prompt engineering and LLM interactionImproving LLM performance on complex tasks such as reasoning, question answering, and code generationDesigning safer and more effective prompting strategies to reduce biases and hallucinationsBuilding AI agents with context engineering and function calling techniquesLearning advanced methods like ReAct, Reflexion, and automatic prompt engineering

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