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README.md
Prompt Engineering Guide
This guide contains a set of papers, learning guides, and tools related to 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.
📣 Full lecture + notebook + exercises on the ~5th of Feb (announcement will happen on Twitter)
📣 Join our Discord to discuss prompt engineering
Table of Contents
Papers
- Surveys / Overviews:
- Approaches/Techniques:
- Batch Prompting: Efficient Inference with LLM APIs (Jan 2023)
- Successive Prompting for Decomposing Complex Questions (Dec 2022)
- Structured Prompting: Scaling In-Context Learning to 1,000 Examples (Dec 2022)
- Large Language Models Are Human-Level Prompt Engineers (Nov 2022)
- Teaching Algorithmic Reasoning via In-context Learning (Nov 2022)
- Ask Me Anything: A simple strategy for prompting language models (Oct 2022)
- Decomposed Prompting: A Modular Approach for Solving Complex Tasks (Oct 2022)
- Promptagator: Few-shot Dense Retrieval From 8 Examples (Sep 2022)
- Large Language Models are Zero-Shot Reasoners (May 2022)
- A Taxonomy of Prompt Modifiers for Text-To-Image Generation (Apr 2022)
- PromptChainer: Chaining Large Language Model Prompts through Visual Programming (Mar 2022)
- Chain of Thought Prompting Elicits Reasoning in Large Language Models (Jan 2022)
- Show Your Work: Scratchpads for Intermediate Computation with Language Models (Nov 2021)
- Reframing Instructional Prompts to GPTk’s Language (Sep 2021)
- Making Pre-trained Language Models Better Few-shot Learners (Aug 2021)
- Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity (April 2021)
- BERTese: Learning to Speak to BERT (April 2021)
- Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm (Feb 2021)
- Calibrate Before Use: Improving Few-Shot Performance of Language Models (Feb 2021)
- Prefix-Tuning: Optimizing Continuous Prompts for Generation (Jan 2021)
- AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts (Oct 2020)
- Language Models are Few-Shot Learners (May 2020)
- How Can We Know What Language Models Know? (July 2020)
- Applications:
- Legal Prompt Engineering for Multilingual Legal Judgement Prediction (Dec 2022)
- Investigating Prompt Engineering in Diffusion Models (Nov 2022)
- Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language (Oct 2022)
- Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic? (Oct 2022)
- Collections:
Tools & Libraries
- OpenAI Playground
- GPTTools
- LangChain
- EveryPrompt
- DUST
- Dyno
- Prompts.ai
- Lexica
- Scale SpellBook
- Interactive Composition Explorer
- LearnGPT
- Promptable
- GPT Index
- Prompt Base
- Playground
- OpenPrompt
- Visual Prompt Builder
- Prompt Generator for OpenAI’s DALL-E 2
- AI Test Kitchen
- betterprompt
- Prompt Engine
- PromptSource
- sharegpt
- DreamStudio
- PromptInject
Datasets
- PartiPrompts
- Real Toxicity Prompts
- DiffusionDB
- P3 - Public Pool of Prompts
- WritingPrompts
- Midjourney Prompts
- Awesome ChatGPT Prompts
- Stable Diffusion Dataset
- Anthropic’s Red Team dataset, (paper)
Blog, Guides, Tutorials and Other Readings
- Pretrain, Prompt, Predict - A New Paradigm for NLP
- Prompt Engineering 101 - Introduction and resources
- Prompt Engineering by co:here
- Prompt Engineering by Microsoft
- Best practices for prompt engineering with OpenAI API
- Start with an Instruction
- CMU Advanced NLP 2022: Prompting
- Prompt Engineering 101: Autocomplete, Zero-shot, One-shot, and Few-shot prompting
- Prompt engineering davinci-003 on our own docs for automated support (Part I)
- DALLE Prompt Book
- DALL·E 2 Prompt Engineering Guide
- Prompt injection attacks against GPT-3
- Language Models and Prompt Engineering: Systematic Survey of Prompting Methods in NLP
- A Complete Introduction to Prompt Engineering for Large Language Models
- Learn Prompting
- 3 Principles for prompt engineering with GPT-3
- Extrapolating to Unnatural Language Processing with GPT-3’s In-context Learning: The Good, the Bad, and the Mysterious
- Prompt Engineering Topic by GitHub
- Prompt Engineering Template
- Awesome ChatGPT Prompts
- Prompt Engineering: From Words to Art
- NLP for Text-to-Image Generators: Prompt Analysis
- Mysteries of mode collapse
- GPT3 and Prompts: A quick primer
- Prompt Engineering in GPT-3
- Talking to machines: prompt engineering & injection
- A beginner-friendly guide to generative language models - LaMBDA guide
- Giving GPT-3 a Turing Test
- Prompts as Programming by Gwern
- AI Content Generation
- How to Draw Anything
- How to write good prompts
- Prompting Methods with Language Models and Their Applications to Weak Supervision
- How to get images that don’t suck
- Best 100+ Stable Diffusion Prompts
- Notes for Prompt Engineering by sw-yx
- A Generic Framework for ChatGPT Prompt Engineering
- Methods of prompt programming
- Prompt Engineering 101
- the Book - Fed Honeypot
- Curtis64’s set of prompt gists
Lecture + Tutorial
Full tutorial and lecture coming soon!
Feel free to open a PR if you think something is missing here. Always welcome feedback and suggestions.
Join our Discord