原文网址：https://github.com/dair-ai/Prompt-Engineering-Guide

    Table of Contents
    ●Lecture
    ●Guides
    ●Papers
    ●Tools & Libraries
    ●Datasets
    ●Blog, Guides, Tutorials and Other Readings
    Lecture
    We have published a 1 hour lecture that provides a comprehensive overview of prompting techniques, applications, and tools.
    ●Video Lecture
    ●Notebook with code
    ●Slides
    Guides
    The following are a set of guides on prompt engineering developed by us. Guides are work in progress.
    ●Prompt Engineering - Introduction
    ●Prompt Engineering - Basic Prompting
    ●Prompt Engineering - Advanced Prompting
    ●Prompt Engineering - Adversarial Prompting
    ●Prompt Engineering - Miscellaneous Topics
    Papers
    The following are the latest papers (sorted by release date) on prompt engineering. We update this on a daily basis and new papers come in. We incorporate summaries of these papers to the guides above every week.
    ●Surveys / Overviews:
    ○Augmented Language Models: a Survey(Feb 2023)
    ○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)
    ●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)
    ○Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning(Sep 2022)
    ○Promptagator: Few-shot Dense Retrieval From 8 Examples(Sep 2022)
    ○DocPrompting: Generating Code by Retrieving the Docs(July 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)
    ●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)
    ○Language Model Crossover: Variation through Few-Shot Prompting(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)
    ●Collections:
    ○Chain-of-ThoughtsPapers
    ○Papers with Code
    ○Prompt Papers
    Tools & Libraries
    (Sorted by Name)
    ●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
    Datasets
    (Sorted by Name)
    ●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
    Blog, Guides, Tutorials and Other Readings
    (Sorted by Name)
    ●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
    ●Unleash Your Creativity with Generative AI: Learn How to Build Innovative Products!
    ●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!

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