# Prompt Engineering Guide
This guide contains a non-exhaustive set of learning guides and tools about prompt engineering. It includes several materials, guides, examples, papers, and much more. The repo is intended to be used as a research and educational reference for practitioners and developers.
## Table of Contents
- [Papers ](#papers )
- [Tools & Libraries ](#tools--libraries )
- [Datasets ](#datasets )
- [Blog, Guides, Tutorials and Other Readings ](#blog-guides-tutorials-and-other-readings )
## Papers
- Surveys / Overviews:
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing ](https://arxiv.org/abs/2107.13586 )
- [A Taxonomy of Prompt Modifiers for Text-To-Image Generation ](https://arxiv.org/abs/2204.13988 )
- [Emergent Abilities of Large Language Models ](https://arxiv.org/abs/2206.07682 )
- Applications:
- [Legal Prompt Engineering for Multilingual Legal Judgement Prediction ](https://arxiv.org/abs/2212.02199 )
- [Investigating Prompt Engineering in Diffusion Models ](https://arxiv.org/abs/2211.15462 )
- [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language ](https://arxiv.org/abs/2210.15157 )
- [Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic? ](https://arxiv.org/abs/2210.14699 )
- Approaches/Techniques:
- [Ask Me Anything: A simple strategy for prompting language models ](https://paperswithcode.com/paper/ask-me-anything-a-simple-strategy-for )
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity ](https://arxiv.org/abs/2104.08786 )
- [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts ](https://arxiv.org/abs/2010.15980 )
- [Large Language Models Are Human-Level Prompt Engineers ](https://sites.google.com/view/automatic-prompt-engineer?pli=1 )
- [Large Language Models are Zero-Shot Reasoners ](https://arxiv.org/abs/2205.11916 )
- [Structured Prompting: Scaling In-Context Learning to 1,000 Examples ](https://arxiv.org/abs/2212.06713 )
- [Chain of Thought Prompting Elicits Reasoning in Large Language Models ](https://arxiv.org/abs/2201.11903 )
- [Reframing Instructional Prompts to GPTk's Language ](https://arxiv.org/abs/2109.07830 )
- [Promptagator: Few-shot Dense Retrieval From 8 Examples ](https://arxiv.org/abs/2209.11755 )
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm ](https://www.arxiv-vanity.com/papers/2102.07350/ )
- [A Taxonomy of Prompt Modifiers for Text-To-Image Generation ](https://arxiv.org/abs/2204.13988 )
- [PromptChainer: Chaining Large Language Model Prompts through Visual Programming ](https://arxiv.org/abs/2203.06566 )
- Collections:
- [Papers with Code ](https://paperswithcode.com/task/prompt-engineering )
- [Prompt Papers ](https://github.com/thunlp/PromptPapers#papers )
## Tools & Libraries
- [OpenAI Playground ](https://beta.openai.com/playground )
- [GPTTools ](https://gpttools.com/comparisontool )
- [EveryPrompt ](https://www.everyprompt.com/ )
- [DUST ](https://dust.tt/ )
- [Prompts.ai ](https://github.com/sevazhidkov/prompts-ai )
- [Lexica ](https://lexica.art/ )
- [Interactive Composition Explorer ](https://github.com/oughtinc/ice )
- [Promptable ](https://promptable.ai/ )
- [GPT Index ](https://github.com/jerryjliu/gpt_index )
- [Prompt Base ](https://promptbase.com/ )
- [Playground ](https://playgroundai.com/ )
- [OpenPrompt ](https://github.com/thunlp/OpenPrompt )
- [Visual Prompt Builder ](https://tools.saxifrage.xyz/prompt )
- [Prompt Generator for OpenAI's DALL-E 2 ](http://dalle2-prompt-generator.s3-website-us-west-2.amazonaws.com/ )
- [AI Test Kitchen ](https://aitestkitchen.withgoogle.com/ )
- [betterprompt ](https://github.com/krrishdholakia/betterprompt )
- [Prompt Engine ](https://github.com/microsoft/prompt-engine )
- [PromptSource ](https://github.com/bigscience-workshop/promptsource )
- [sharegpt ](https://sharegpt.com/ )
- [DreamStudio ](https://beta.dreamstudio.ai/ )
## Datasets
- [PartiPrompts ](https://parti.research.google/ )
- [Real Toxicity Prompts ](https://allenai.org/data/real-toxicity-prompts )
- [DiffusionDB ](https://github.com/poloclub/diffusiondb )
- [P3 - Public Pool of Prompts ](https://huggingface.co/datasets/bigscience/P3 )
- [WritingPrompts ](WritingPrompts )
- [Midjourney Prompts ](https://huggingface.co/datasets/succinctly/midjourney-prompts )
- [Awesome ChatGPT Prompts ](https://huggingface.co/datasets/fka/awesome-chatgpt-prompts )
- [Stable Diffusion Dataset ](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts )
## Blog, Guides, Tutorials and Other Readings
- [Prompt Engineering 101 - Introduction and resources ](https://www.linkedin.com/pulse/prompt-engineering-101-introduction-resources-amatriain/ )
- [Prompt Engineering by co:here ](https://docs.cohere.ai/docs/prompt-engineering )
- [Prompt Engineering by Microsoft ](https://microsoft.github.io/prompt-engineering/ )
- [Best practices for prompt engineering with OpenAI API ](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api )
- [Start with an Instruction ](https://beta.openai.com/docs/quickstart/start-with-an-instruction )
- [CMU Advanced NLP 2022: Prompting ](https://youtube.com/watch?v=5ef83Wljm-M&feature=shares )
- [Prompt Engineering 101: Autocomplete, Zero-shot, One-shot, and Few-shot prompting ](https://youtube.com/watch?v=v2gD8BHOaX4&feature=shares )
- [Prompt engineering davinci-003 on our own docs for automated support (Part I) ](https://www.patterns.app/blog/2022/12/21/finetune-llm-tech-support/ )
- [DALLE Prompt Book ](https://dallery.gallery/the-dalle-2-prompt-book/ )
- [DALL·E 2 Prompt Engineering Guide ](https://docs.google.com/document/d/11WlzjBT0xRpQhP9tFMtxzd0q6ANIdHPUBkMV-YB043U/edit# )
- [Prompt injection attacks against GPT-3 ](https://simonwillison.net/2022/Sep/12/prompt-injection/ )
- [Language Models and Prompt Engineering: Systematic Survey of Prompting Methods in NLP ](https://youtube.com/watch?v=OsbUfL8w-mo&feature=shares )
- [A Complete Introduction to Prompt Engineering for Large Language Models ](https://www.mihaileric.com/posts/a-complete-introduction-to-prompt-engineering/ )
- [Learn Prompting ](https://learnprompting.org/ )
- [3 Principles for prompt engineering with GPT-3 ](https://www.linkedin.com/pulse/3-principles-prompt-engineering-gpt-3-ben-whately/ )
- [Extrapolating to Unnatural Language Processing with GPT-3's In-context Learning: The Good, the Bad, and the Mysterious ](http://ai.stanford.edu/blog/in-context-learning/ )
- [Prompt Engineering Topic by GitHub ](https://github.com/topics/prompt-engineering )
- [Prompt Engineering Template ](https://docs.google.com/spreadsheets/d/1-snKDn38-KypoYCk9XLPg799bHcNFSBAVu2HVvFEAkA/edit#gid=0 )
- [Awesome ChatGPT Prompts ](https://github.com/f/awesome-chatgpt-prompts )
- [Prompt Engineering: From Words to Art ](https://www.saxifrage.xyz/post/prompt-engineering )
- [NLP for Text-to-Image Generators: Prompt Analysis ](https://heartbeat.comet.ml/nlp-for-text-to-image-generators-prompt-analysis-part-1-5076a44d8365 )
- [GPT3 and Prompts: A quick primer ](https://buildspace.so/notes/intro-to-gpt3-prompts )
- [Prompt Engineering in GPT-3 ](https://www.analyticsvidhya.com/blog/2022/05/prompt-engineering-in-gpt-3/ )
- [Talking to machines: prompt engineering & injection ](https://artifact-research.com/artificial-intelligence/talking-to-machines-prompt-engineering-injection/ )
- [A beginner-friendly guide to generative language models - LaMBDA guide ](https://aitestkitchen.withgoogle.com/how-lamda-works )
- [Giving GPT-3 a Turing Test ](https://lacker.io/ai/2020/07/06/giving-gpt-3-a-turing-test.html )
- [Prompts as Programming by Gwern ](https://www.gwern.net/GPT-3#prompts-as-programming )
- [AI Content Generation ](https://www.jonstokes.com/p/ai-content-generation-part-1-machine )
- [How to Draw Anything ](https://andys.page/posts/how-to-draw/ )
- [How to write good prompts ](https://andymatuschak.org/prompts/ )
- [Prompting Methods with Language Models and Their Applications to Weak Supervision ](https://snorkel.ai/prompting-methods-with-language-models-nlp/ )
- [How to get images that don't suck ](https://www.reddit.com/r/StableDiffusion/comments/x41n87/how_to_get_images_that_dont_suck_a/ )
- [Best 100+ Stable Diffusion Prompts ](https://mpost.io/best-100-stable-diffusion-prompts-the-most-beautiful-ai-text-to-image-prompts/ )
- [Notes for Prompt Engineering by sw-yx ](https://github.com/sw-yx/ai-notes )
# Lecture + Tutorial
Full tutorial and lecture coming soon! If you would like to sponsor this open initiative reach out on [Twitter ](https://twitter.com/omarsar0 ) or at ellfae@gmail.com.
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Feel free to open a PR if you think something is missing here. Always welcome feedback and suggestions.