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dair-ai Prompt-Engineering-Guide

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

Stars

77,993

Popular

Forks

8,566

Watchers: 77,993

Language

MDX

License: MIT License

Repository Radar Score

51 / 100

Growth

7d
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30d
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Not enough metric snapshots yet to chart growth for this repository.

Score breakdown

  • popularity 90
  • growth 0
  • activity 35
  • freshness 100
  • community 70

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Prompt Engineering Guide

Sponsored by    

Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs). Researchers use prompt engineering to improve the capacity of LLMs on a wide range of common and complex tasks such as question answering and arithmetic reasoning. Developers use prompt engineering to design robust and effective prompting techniques that interface with LLMs and other tools.

Motivated by the high interest in developing with LLMs, we have created this new prompt engineering guide that contains all the latest papers, learning guides, lectures, references, and tools related to prompt engineering for LLMs.

🌐 Prompt Engineering Guide (Web Version)

🎉 We are excited to launch our new prompt engineering, RAG, and AI Agents courses under the DAIR.AI Academy. Join Now!

The courses are meant to compliment this guide and provide a more hands-on approach to learning about prompt engineering, context engineering, and AI Agents.

Use code PROMPTING20 to get an extra 20% off.

Happy Prompting!


Announcements / Updates

  • 🎓 We now offer self-paced prompt engineering courses under our DAIR.AI Academy. Join Now!
  • 🎓 New course on Prompt Engineering for LLMs announced! Enroll here!
  • 💼 We now offer several services like corporate training, consulting, and talks.
  • 🌐 We now support 13 languages! Welcoming more translations.
  • 👩‍🎓 We crossed 3 million learners in January 2024!
  • 🎉 We have launched a new web version of the guide here
  • 🔥 We reached #1 on Hacker News on 21 Feb 2023
  • 🎉 The First Prompt Engineering Lecture went live here

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Guides

You can also find the most up-to-date guides on our new website https://www.promptingguide.ai/.


Lecture

We have published a 1 hour lecture that provides a comprehensive overview of prompting techniques, applications, and tools.


Running the guide locally

To run the guide locally, for example to check the correct implementation of a new translation, you will need to:

  1. Install Node >=18.0.0
  2. Install pnpm if not present in your system. Check here for detailed instructions.
  3. Install the dependencies: pnpm i next react react-dom nextra nextra-theme-docs
  4. Boot the guide with pnpm dev
  5. Browse the guide at http://localhost:3000/

Appearances

Some places where we have been featured:


If you are using the guide for your work or research, please cite us as follows:

@article{Saravia_Prompt_Engineering_Guide_2022,
author = {Saravia, Elvis},
journal = {https://github.com/dair-ai/Prompt-Engineering-Guide},
month = {12},
title = {{Prompt Engineering Guide}},
year = {2022}
}

License

MIT License

Feel free to open a PR if you think something is missing here. Always welcome feedback and suggestions. Just open an issue!

Created: Dec 16, 2022

Last push: Mar 11, 2026

Default branch: main

Languages

Share of the codebase by language, based on repository metadata from the host.

  • MDX 94.5%
  • Jupyter Notebook 4.0%
  • TypeScript 1.1%
  • HTML 0.4%
  • JavaScript 0.1%
  • CSS 0.0%

Repository Radar analysis

Deterministic insights derived from public metadata and our observations — not personal testing or reviews.

Why this repository is interesting

  • High absolute popularity (77,993 stars) signals broad adoption.

Who should use it

  • Developers working primarily with MDX
  • Teams exploring AI tooling, agents, or ML infrastructure

Potential use cases

  • Reference or evaluate MDX open-source approaches in this domain
  • Prototype AI/agent workflows or study reference architectures

Strengths

  • README present in our index
  • Declared license: MIT License
  • Substantial fork count (8,566) suggests reuse and contribution interest

Limitations / considerations

  • Insights are derived from public metadata and our observations — not a substitute for code review

What to watch

  • Re-check last push, issues, and releases on GitHub before production adoption

Strong signals: Strong community interest

Source: GitHub (public metadata) + Repository Radar analysis. We do not claim ownership of third-party repositories.

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