Prompt Engineering Guide
A free online guide, papers list, and notebooks for learning how to prompt and build with language models.
- Stars
- 79.8k
- Forks
- 8.8k
- Licence
- MIT
- Language
- MDX
- Last update
- 8 months ago
- Type
- Learning resource
What it is
This is a collection of guides, research papers, lectures, notebooks and reference links about prompt engineering, the practice of writing and structuring inputs to get better results from large language models. The README also covers related topics such as context engineering, retrieval augmented generation (RAG), and AI agents. It is mainly a reading resource published as a website at promptingguide.ai, not a software program you install.
- Read the introduction and basics sections to learn how prompts are structured
- Work through technique pages such as few-shot prompting or self-consistency and try the examples yourself
- Browse the papers and notebooks section to find research on a specific prompting method
- Use the prompt hub examples as starting points for classification, summarization or coding tasks
Great for
- People who are new to prompting and want structured lessons with examples
- Developers who want an overview of prompting techniques like chain-of-thought and ReAct
- Researchers looking for a curated list of prompt engineering papers
- Teachers or students building a course around language model basics
Not ideal for
- Anyone looking for a software tool to install, since the README describes a guide and resource collection rather than an application
- Readers who need up-to-date model-specific details, because the README does not say how often the content is reviewed and some model pages may age quickly
- People who want a hands-on, paid course; the README promotes DAIR.AI Academy courses separately and those are not part of this repository
How to get it
- 1
Read it online
Clone the repository to read the source files locally
git clone https://github.com/dair-ai/Prompt-Engineering-Guide - 2
Run it
- Open https://www.promptingguide.ai/ in your browser
- Start with the Introduction section and read the basics of prompting
- Move to the Techniques section and try examples in your preferred chat model
- Explore the Papers or Notebooks section when you want deeper material
- 3
Get the most out of it
- Read the Introduction section first, then move to Techniques in the order the guide lists them
- Use the Prompt Hub examples as templates and adapt them to your own tasks
- Check the Papers section for primary sources behind each technique
- Follow the Notebooks section if you want to run code examples yourself
Downloads
Strengths
- Covers a broad range of topics, from beginner basics to techniques like RAG and agents
- Organized into clear sections for guides, techniques, papers, and notebooks
- Free to read online and the repository is MIT licensed
Trade-offs
- The README is a list of links, so the depth of each topic depends on the linked web pages, which this analysis did not review
- Some README content is promotional, including sponsorship and course discounts
- The README does not state how often the material is updated
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| Prompt-Engineering-Guide (this) | 79.8k | Learning resource | beginner | 8 months ago | People who are new to prompting and want structured lessons with examples |
| openai-cookbook | 76.9k | Learning resource | beginner | 5 days ago | Developers who are new to the OpenAI API and want working examples |
| generative-ai-for-beginners | 123k | Learning resource | beginner | 5 days ago | Developers who know some Python or TypeScript and want a guided introduction to generative AI |
| ai-engineering-from-scratch | 76.3k | Learning resource | intermediate | 4 days ago | Students who want to understand how LLMs, retrieval, and agents work internally |
| langchain | 149k | Framework | intermediate | 4 days ago | Python developers building LLM-powered applications |
| langgraph | 44.3k | Library | intermediate | 4 days ago | Python developers building multi-step AI agents that must survive failures or long waits |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.