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提示工程是一种相对较新的学科，用于开发和优化提示，以有效地使用语言模型（LM）进行各种应用和研究主题。提示工程技能有助于更好地理解大型语言模型（LLM）的能力和局限性。研究人员使用提示工程来改善LLMs在各种常见和复杂任务上的能力，如问答和算术推理。开发人员使用提示工程来设计与LLMs和其他工具进行交互的稳健而有效的提示技术。 受到对LLMs开发的高度关注的启发，我们创建了这个新的提示工程指南，其中包含了与提示工程相关的所有最新论文、学习指南、讲座、参考资料和工具。
Happy Prompting!
- 🔥我们在2023年2月21日荣登Hacker News排名第一！
- 🎉 Prompt Engineering讲座已经上线，链接在这里。 here
- 🎓 我们正在创建一套全面的指南，链接在#guides。 here
- 📖 即将推出一系列深入教程，敬请期待！
- 讲座Lecture
- 指南Guides
- 论文Papers
- 工具和库Tools & Libraries
- 数据集Datasets
- 博客、指南、教程和其他阅读材料Blog, Guides, Tutorials and Other Readings
我们已经发布了一篇为期1小时的讲座，提供了提示技术、应用和工具的全面概述。
以下是我们开发的一套有关提示工程的指南。指南仍在不断完善中。
以下是有关提示工程的最新论文（按发布日期排序）。我们每天都会更新此内容，新论文不断涌现。我们每周会将这些论文的摘要纳入上面的指南中。
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Surveys / Overviews:
- 增强语言模型：一项调查 (Feb 2023)
- 上下文学习的调查 (Dec 2022)
- 迈向大型语言模型推理：一项调查 (Dec 2022)
- 大型语言模型的新兴能力 (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:
- Active Prompting with Chain-of-Thought for Large Language Models (Feb 2023)
- More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models (Feb 2023)
- Guiding Large Language Models via Directional Stimulus Prompting (Feb 2023)
- How Does In-Context Learning Help Prompt Tuning? (Feb 2023)
- Scalable Prompt Generation for Semi-supervised Learning with Language Models (Feb 2023)
- Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints (Feb 2023)
- À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting (Feb 2023)
- GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks (Feb 2023)
- The Capacity for Moral Self-Correction in Large Language Models (Feb 2023)
- SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains (Feb 2023)
- Evaluating the Robustness of Discrete Prompts (Feb 2023)
- Compositional Exemplars for In-context Learning (Feb 2023)
- Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery (Feb 2023)
- 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)
- Learning to Transfer Prompts for Text Generation (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)
- Training language models to follow instructions with human feedback
- 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)
- Multitask Prompted Training Enables Zero-Shot Task Generalization (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:
- How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study (Feb 2023)
- Grimm in Wonderland: Prompt Engineering with Midjourney to Illustrate Fairytales (Feb 2023)
- LabelPrompt: Effective Prompt-based Learning for Relation Classification (Feb 2023)
- Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition (Feb 2023)
- The Capacity for Moral Self-Correction in Large Language Models (Feb 2023)
- Prompting for Multimodal Hateful Meme Classification (Feb 2023)
- PLACES: Prompting Language Models for Social Conversation Synthesis (Feb 2023)
- Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation (Feb 2023)
- Crawling the Internal Knowledge-Base of Language Models (Jan 2023)
- Legal Prompt Engineering for Multilingual Legal Judgement Prediction (Dec 2022)
- Investigating Prompt Engineering in Diffusion Models (Nov 2022)
- Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering (Sep 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
- Promptly
- PromptSource
- Promptist
- 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
- 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
- AI's rise generates new job title: Prompt engineer
- Awesome ChatGPT Prompts
- Best 100+ Stable Diffusion Prompts
- Best practices for prompt engineering with OpenAI API
- Building GPT-3 applications — beyond the prompt
- 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
- DALL·E 2 Preview - Risks and Limitations
- DALLE Prompt Book
- DALL-E, Make Me Another Picasso, Please
- Diffusion Models: A Practical Guide
- 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
- GPT-3 & Beyond
- GPT3 and Prompts: A quick primer
- How to Draw Anything
- How to get images that don't suck
- How to make LLMs say true things
- How to write good prompts
- Introduction to Reinforcement Learning with Human Feedback
- In defense of prompt engineering
- 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
- NLP with Deep Learning CS224N/Ling284 - Lecture 11: Promting, Instruction Tuning, and RLHF
- Notes for Prompt Engineering by sw-yx
- OpenAI Cookbook
- OpenAI Prompt Examples for several applications
- 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 - A new profession ?
- Prompt Engineering by co:here
- Prompt Engineering by Microsoft
- Prompt Engineering: The Career of Future
- 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 Engineering with OpenAI's GPT-3 and other LLMs
- Prompt injection attacks against GPT-3
- Prompt injection to read out the secret OpenAI API key
- Prompting in NLP: Prompt-based zero-shot learning
- Prompting Methods with Language Models and Their Applications to Weak Supervision
- Prompts as Programming by Gwern
- Reverse Prompt Engineering for Fun and (no) Profit
- So you want to be a prompt engineer: Critical careers of the future
- Simulators
- Start with an Instruction
- Talking to machines: prompt engineering & injection
- The Book - Fed Honeypot
- The ChatGPT Prompt Book
- The Mirror of Language
- Using GPT-Eliezer against ChatGPT Jailbreaking
- What Is ChatGPT Doing … and Why Does It Work?
If you are using the guide for your work, 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}
}
Feel free to open a PR if you think something is missing here. Always welcome feedback and suggestions. Just open an issue!