top 3 repos on github for prompt engineering: → dair-ai/Prompt-Engineering-Guide - 70k http://github.com/dair-ai/Prompt-Engineering-Guide… → anthropics/anthropic-cookbook - 12k http://github.com/anthropics/anthropic-cookbook… → promptslab/Awesome-Prompt-Engineering - 10k http://github.com/promptslab/Awesome-Prompt-Engineering… all free. bookmark this.

dair-ai/Prompt-Engineering-Guide

Source: https://github.com/dair-ai/Prompt-Engineering-Guide

Prompt Engineering Guide

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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!

Guides

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

• Prompt Engineering - Introduction
  • Prompt Engineering - LLM Settings
  • Prompt Engineering - Basics of Prompting
  • Prompt Engineering - Prompt Elements
  • Prompt Engineering - General Tips for Designing Prompts
  • Prompt Engineering - Examples of Prompts
• Prompt Engineering - Techniques
  • Prompt Engineering - Zero-Shot Prompting
  • Prompt Engineering - Few-Shot Prompting
  • Prompt Engineering - Chain-of-Thought Prompting
  • Prompt Engineering - Self-Consistency
  • Prompt Engineering - Generate Knowledge Prompting
  • Prompt Engineering - Prompt Chaining
  • Prompt Engineering - Tree of Thoughts (ToT)
  • Prompt Engineering - Retrieval Augmented Generation
  • Prompt Engineering - Automatic Reasoning and Tool-use (ART)
  • Prompt Engineering - Automatic Prompt Engineer
  • Prompt Engineering - Active-Prompt
  • Prompt Engineering - Directional Stimulus Prompting
  • Prompt Engineering - Program-Aided Language Models
  • Prompt Engineering - ReAct Prompting
  • Prompt Engineering - Multimodal CoT Prompting
  • Prompt Engineering - Graph Prompting
• Prompt Engineering - Applications
  • Prompt Engineering - Function Calling
  • Prompt Engineering - Generating Data
  • Prompt Engineering - Generating Synthetic Dataset for RAG
  • Prompt Engineering - Takling Generated Datasets Diversity
  • Prompt Engineering - Generating Code
  • Prompt Engineering - Graduate Job Classification Case Study
• Prompt Engineering - Prompt Hub
  • Prompt Engineering - Classification
  • Prompt Engineering - Coding
  • Prompt Engineering - Creativity
  • Prompt Engineering - Evaluation
  • Prompt Engineering - Information Extraction
  • Prompt Engineering - Image Generation
  • Prompt Engineering - Mathematics
  • Prompt Engineering - Question Answering
  • Prompt Engineering - Reasoning
  • Prompt Engineering - Text Summarization
  • Prompt Engineering - Truthfulness
  • Prompt Engineering - Adversarial Prompting
• Prompt Engineering - Models
  • Prompt Engineering - ChatGPT
  • Prompt Engineering - Code Llama
  • Prompt Engineering - Flan
  • Prompt Engineering - Gemini
  • Prompt Engineering - GPT-4
  • Prompt Engineering - LLaMA
  • Prompt Engineering - Mistral 7B
  • Prompt Engineering - Mixtral
  • Prompt Engineering - OLMo
  • Prompt Engineering - Phi-2
  • Prompt Engineering - Model Collection
• Prompt Engineering - Risks and Misuses
  • Prompt Engineering - Adversarial Prompting
  • Prompt Engineering - Factuality
  • Prompt Engineering - Biases
• Prompt Engineering - Papers
  • Prompt Engineering - Overviews
  • Prompt Engineering - Approaches
  • Prompt Engineering - Applications
  • Prompt Engineering - Collections
• Prompt Engineering - Tools
• Prompt Engineering - Notebooks
• Prompt Engineering - Datasets
• Prompt Engineering - Additional Readings

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/

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!