This guide contains a set of recent papers, learning guides, and tools related to prompt engineering. The repo is intended as a research and educational reference for practitioners and developers.
Announcements:
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📣 Full lecture + notebook + exercises on the ~15th of Feb (announcement will happen on Twitter)
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📣 Examples and explainers coming soon! (reach out if you want to contribute)
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Join our Discord to discuss more about prompt engineering
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Surveys / Overviews:
- A Survey for In-context Learning (Dec 2022)
- Towards Reasoning in Large Language Models: A Survey (Dec 2022)
- Emergent Abilities of Large Language Models (Jun 2022)
- A Taxonomy of Prompt Modifiers for Text-To-Image Generation (Apr 2022)
- Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (Jul 2021)
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Approaches/Techniques:
- Multimodal Chain-of-Thought Reasoning in Language Models (Feb 2023)
- Large Language Models Can Be Easily Distracted by Irrelevant Context (Feb 2023)
- Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models (Feb 2023)
- Progressive Prompts: Continual Learning for Language Models (Jan 2023)
- Batch Prompting: Efficient Inference with LLM APIs (Jan 2023)
- On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning (Dec 2022)
- Constitutional AI: Harmlessness from AI Feedback (Dec 2022)
- Successive Prompting for Decomposing Complex Questions (Dec 2022)
- Discovering Language Model Behaviors with Model-Written Evaluations (Dec 2022)
- Structured Prompting: Scaling In-Context Learning to 1,000 Examples (Dec 2022)
- PAL: Program-aided Language Models (Nov 2022)
- Large Language Models Are Human-Level Prompt Engineers (Nov 2022)
- Ignore Previous Prompt: Attack Techniques For Language Models (Nov 2022)
- Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods (Nov 2022)
- Teaching Algorithmic Reasoning via In-context Learning (Nov 2022)
- Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference (Nov 2022)
- Ask Me Anything: A simple strategy for prompting language models (Oct 2022)
- ReAct: Synergizing Reasoning and Acting in Language Models (Oct 2022)
- Prompting GPT-3 To Be Reliable (Oct 2022)
- Decomposed Prompting: A Modular Approach for Solving Complex Tasks (Oct 2022)
- Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought (Oct 2022)
- Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples (Sep 2022)
- Promptagator: Few-shot Dense Retrieval From 8 Examples (Sep 2022)
- On the Advance of Making Language Models Better Reasoners (June 2022)
- Large Language Models are Zero-Shot Reasoners (May 2022)
- MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning (May 2022)
- Toxicity Detection with Generative Prompt-based Inference (May 2022)
- The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning (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)
- Self-Consistency Improves Chain of Thought Reasoning in Language Models (March 2022)
- Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? (Feb 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)
- Generated Knowledge Prompting for Commonsense Reasoning (Oct 2021)
- Reframing Instructional Prompts to GPTk's Language (Sep 2021)
- Design Guidelines for Prompt Engineering Text-to-Image Generative Models (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)
- The Power of Scale for Parameter-Efficient Prompt Tuning (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)
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Applications:
- Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation (Feb 2023)
- 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)
- Plot Writing From Scratch Pre-Trained Language Models (July 2022)
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Collections:
- AI Test Kitchen
- betterprompt
- DreamStudio
- DUST
- Dyno
- EveryPrompt
- GPT Index
- GPTTools
- hwchase17/adversarial-prompts
- Interactive Composition Explorer
- LangChain
- LearnGPT
- Lexica
- loom
- Metaprompt
- OpenAI Playground
- OpenPrompt
- Playground
- Prodia
- Prompt Base
- Prompt Engine
- Prompt Generator for OpenAI's DALL-E 2
- Promptable
- PromptInject
- Prompts.ai
- PromptSource
- Scale SpellBook
- sharegpt
- ThoughtSource
- Visual Prompt Builder
- Anthropic's Red Team dataset, (paper)
- Awesome ChatGPT Prompts
- DiffusionDB
- Midjourney Prompts
- P3 - Public Pool of Prompts
- PartiPrompts
- Real Toxicity Prompts
- Stable Diffusion Dataset
- WritingPrompts
- OpenAI Cookbook
- 3 Principles for prompt engineering with GPT-3
- A beginner-friendly guide to generative language models - LaMBDA guide
- A Complete Introduction to Prompt Engineering for Large Language Models
- A Generic Framework for ChatGPT Prompt Engineering
- AI Content Generation
- Awesome ChatGPT Prompts
- Best 100+ Stable Diffusion Prompts
- Best practices for prompt engineering with OpenAI API
- ChatGPT, AI and GPT-3 Apps and use cases
- CMU Advanced NLP 2022: Prompting
- Curtis64's set of prompt gists
- DALL·E 2 Prompt Engineering Guide
- DALLE Prompt Book
- Exploiting GPT-3 Prompts
- Exploring Prompt Injection Attacks
- Extrapolating to Unnatural Language Processing with GPT-3's In-context Learning: The Good, the Bad, and the Mysterious
- Generative AI with Cohere: Part 1 - Model Prompting
- Giving GPT-3 a Turing Test
- GPT3 and Prompts: A quick primer
- How to Draw Anything
- How to get images that don't suck
- How to write good prompts
- Language Models and Prompt Engineering: Systematic Survey of Prompting Methods in NLP
- Learn Prompting
- Methods of prompt programming
- Mysteries of mode collapse
- NLP for Text-to-Image Generators: Prompt Analysis
- Notes for Prompt Engineering by sw-yx
- Pretrain, Prompt, Predict - A New Paradigm for NLP
- Prompt Engineering 101 - Introduction and resources
- Prompt Engineering 101: Autocomplete, Zero-shot, One-shot, and Few-shot prompting
- Prompt Engineering 101
- Prompt Engineering by co:here
- Prompt Engineering by Microsoft
- Prompt engineering davinci-003 on our own docs for automated support (Part I)
- Prompt Engineering Guide: How to Engineer the Perfect Prompts
- Prompt Engineering in GPT-3
- Prompt Engineering Template
- Prompt Engineering Topic by GitHub
- Prompt Engineering: From Words to Art
- Prompt injection attacks against GPT-3
- Prompt injection to read out the secret OpenAI API key
- Prompting Methods with Language Models and Their Applications to Weak Supervision
- Prompts as Programming by Gwern
- Reverse Prompt Engineering for Fun and (no) Profit
- Simulators
- Start with an Instruction
- Talking to machines: prompt engineering & injection
- the Book - Fed Honeypot
- The ChatGPT Prompt Book
- Using GPT-Eliezer against ChatGPT Jailbreaking
Full tutorial and lecture coming soon!
Feel free to open a PR if you think something is missing here. Always welcome feedback and suggestions.
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