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Prompt Engineering Guide

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.

Happy Prompting!


Announcements / Updates

  • 🔥 We reached #1 on Hacker News on 21 Feb 2023
  • 🎉 The Prompt Engineering Lecture went live here
  • 🎓 We're creating a set of comprehensive guides here
  • 📖 New set of deep tutorials coming soon!

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Table of Contents


Lecture

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


Guides

The following are a set of guides on prompt engineering developed by us. Guides are work in progress.


Papers

The following are the latest papers (sorted by release date) on prompt engineering. We update this on a daily basis and new papers come in. We incorporate summaries of these papers to the guides above every week.


Tools & Libraries

(Sorted by Name)


Datasets

(Sorted by Name)


Blog, Guides, Tutorials and Other Readings

(Sorted by Name)


If you are using the guide for your work, 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}
}

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

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