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