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提示工程的指南、论文、讲座和资源，提示词逆向工程
https://github.com/PartnerDAO/Prompt-Engineering-Guide-zh
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提示工程的指南、论文、讲座和资源，提示词逆向工程
Host: GitHub
URL: https://github.com/PartnerDAO/Prompt-Engineering-Guide-zh
Owner: PartnerDAO
License: mit
Fork: true (dair-ai/Prompt-Engineering-Guide)
Created: 2023-02-25T06:16:31.000Z (over 1 year ago)
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Last Pushed: 2023-03-04T11:35:17.000Z (over 1 year ago)
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- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE.md
- Citation: CITATION.cff
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README

        # 提示词工程指南-中文版

本文由PartnerDAO翻译，实践案例更新在《AIGC时代：超级个体的崛起》中，地址（https://xiaobot.net/p/SuperIndividual?refer=5e3a5f9b-4de3-47cd-b41f-04566098cfe3）

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!

---

## Announcements / Updates

- 🔥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](https://youtu.be/dOxUroR57xs)

- 🎓  我们正在创建一套全面的指南，链接在#guides。 [here](#guides)

- 📖 即将推出一系列深入教程，敬请期待！

[Join our Discord](https://discord.gg/SKgkVT8BGJ)

[Follow us on Twitter](https://twitter.com/dair_ai)

---

## 目录

- 讲座[Lecture](#lecture)

- 指南[Guides](#guides)

- 论文[Papers](#papers)

- 工具和库[Tools & Libraries](#tools--libraries)

- 数据集[Datasets](#datasets)

- 博客、指南、教程和其他阅读材料[Blog, Guides, Tutorials and Other Readings](#blog-guides-tutorials-and-other-readings)

---

## 讲座

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。

- [Video Lecture](https://youtu.be/dOxUroR57xs)

- [Notebook with code](https://github.com/dair-ai/Prompt-Engineering-Guide/blob/main/notebooks/pe-lecture.ipynb)

- [Slides](https://github.com/dair-ai/Prompt-Engineering-Guide/blob/main/lecture/Prompt-Engineering-Lecture-Elvis.pdf)

---

## 指南

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。  

- [提示工程 - 简介](/guides/prompts-intro.md)

- [提示工程 - 基础提示](/guides/prompts-basic-usage.md)

- [提示工程 - 高级提示](/guides/prompts-advanced-usage.md)

- [提示工程 - 对抗提示](/guides/prompt-adversarial.md)

- [提示工程 - 杂项主题](/guides/prompt-miscellaneous.md)

---

## 论文

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。

- Surveys / Overviews:

  - [增强语言模型：一项调查](https://arxiv.org/abs/2302.07842) (Feb 2023)

  - [上下文学习的调查](https://arxiv.org/abs/2301.00234) (Dec 2022)

  - [迈向大型语言模型推理：一项调查](https://arxiv.org/abs/2212.10403) (Dec 2022)

  - [大型语言模型的新兴能力](https://arxiv.org/abs/2206.07682) (Jun 2022)

  - [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988) (Apr 2022)

  - [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://arxiv.org/abs/2107.13586) (Jul 2021)

- Approaches/Techniques:

  

  - [Active Prompting with Chain-of-Thought for Large Language Models](https://arxiv.org/abs/2302.12246) (Feb 2023)

  - [More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models](https://arxiv.org/abs/2302.12173) (Feb 2023)

  - [Guiding Large Language Models via Directional Stimulus Prompting](https://arxiv.org/abs/2302.11520) (Feb 2023)

  - [How Does In-Context Learning Help Prompt Tuning?](https://arxiv.org/abs/2302.11521) (Feb 2023)

  - [Scalable Prompt Generation for Semi-supervised Learning with Language Models](https://arxiv.org/abs/2302.09236) (Feb 2023)

  - [Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints](https://arxiv.org/abs/2302.09185) (Feb 2023)

  - [À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting](https://arxiv.org/abs/2302.07994) (Feb 2023)

  - [GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks](https://arxiv.org/abs/2302.08043) (Feb 2023)

  - [The Capacity for Moral Self-Correction in Large Language Models](https://arxiv.org/abs/2302.07459) (Feb 2023)

  - [SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains](https://arxiv.org/abs/2302.06868) (Feb 2023)

  - [Evaluating the Robustness of Discrete Prompts](https://arxiv.org/abs/2302.05619) (Feb 2023)

  - [Compositional Exemplars for In-context Learning](https://arxiv.org/abs/2302.05698) (Feb 2023)

  - [Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery](https://arxiv.org/abs/2302.03668) (Feb 2023)

  - [Multimodal Chain-of-Thought Reasoning in Language Models](https://arxiv.org/abs/2302.00923) (Feb 2023)

  - [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://arxiv.org/abs/2302.00093) (Feb 2023)

  - [Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models](https://arxiv.org/abs/2302.00618) (Feb 2023)

  - [Progressive Prompts: Continual Learning for Language Models](https://arxiv.org/abs/2301.12314) (Jan 2023)

  - [Batch Prompting: Efficient Inference with LLM APIs](https://arxiv.org/abs/2301.08721) (Jan 2023)

  - [On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://arxiv.org/abs/2212.08061) (Dec 2022)

  - [Constitutional AI: Harmlessness from AI Feedback](https://arxiv.org/abs/2212.08073) (Dec 2022)

  - [Successive Prompting for Decomposing Complex Questions](https://arxiv.org/abs/2212.04092) (Dec 2022)

  - [Discovering Language Model Behaviors with Model-Written Evaluations](https://arxiv.org/abs/2212.09251) (Dec 2022)

  - [Structured Prompting: Scaling In-Context Learning to 1,000 Examples](https://arxiv.org/abs/2212.06713) (Dec 2022)

  - [PAL: Program-aided Language Models](https://arxiv.org/abs/2211.10435) (Nov 2022)

  - [Large Language Models Are Human-Level Prompt Engineers](https://arxiv.org/abs/2211.01910) (Nov 2022)

  - [Ignore Previous Prompt: Attack Techniques For Language Models](https://arxiv.org/abs/2211.09527) (Nov 2022)

  - [Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods](https://arxiv.org/abs/2210.07321) (Nov 2022)

  - [Teaching Algorithmic Reasoning via In-context Learning](https://arxiv.org/abs/2211.09066) (Nov 2022)

  - [Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference](https://arxiv.org/abs/2211.11875) (Nov 2022)

  - [Ask Me Anything: A simple strategy for prompting language models](https://paperswithcode.com/paper/ask-me-anything-a-simple-strategy-for) (Oct 2022)

  - [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) (Oct 2022)

  - [Prompting GPT-3 To Be Reliable](https://arxiv.org/abs/2210.09150) (Oct 2022)

  - [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://arxiv.org/abs/2210.02406) (Oct 2022)

  - [Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought](https://arxiv.org/abs/2210.01240v3) (Oct 2022)

  - [Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples](https://arxiv.org/abs/2209.02128) (Sep 2022)

  - [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://arxiv.org/abs/2209.11755) (Sep 2022)

  - [On the Advance of Making Language Models Better Reasoners](https://arxiv.org/abs/2206.02336) (June 2022)

  - [Large Language Models are Zero-Shot Reasoners](https://arxiv.org/abs/2205.11916) (May 2022)

  - [MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning](https://arxiv.org/abs/2205.00445) (May 2022)

  - [Toxicity Detection with Generative Prompt-based Inference](https://arxiv.org/abs/2205.12390) (May 2022)

  - [Learning to Transfer Prompts for Text Generation](https://arxiv.org/abs/2205.01543) (May 2022)

  - [The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning](https://arxiv.org/abs/2205.03401) (May 2022)

  - [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988) (Apr 2022)

  - [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://arxiv.org/abs/2203.06566) (Mar 2022)

  - [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://arxiv.org/abs/2203.11171) (March 2022)

  - [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)

  - [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://arxiv.org/abs/2202.12837) (Feb 2022)

  - [Chain of Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903) (Jan 2022)

  - [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://arxiv.org/abs/2112.00114) (Nov 2021)

  - [Generated Knowledge Prompting for Commonsense Reasoning](https://arxiv.org/abs/2110.08387) (Oct 2021)

  - [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) (Oct 2021)

  - [Reframing Instructional Prompts to GPTk's Language](https://arxiv.org/abs/2109.07830) (Sep 2021)

  - [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://arxiv.org/abs/2109.06977) (Sep 2021)

  - [Making Pre-trained Language Models Better Few-shot Learners](https://aclanthology.org/2021.acl-long.295) (Aug 2021)

  - [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://arxiv.org/abs/2104.08786) (April 2021)

  - [BERTese: Learning to Speak to BERT](https://aclanthology.org/2021.eacl-main.316) (April 2021)

  - [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/abs/2104.08691) (April 2021)

  - [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://arxiv.org/abs/2102.07350) (Feb 2021)

  - [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://arxiv.org/abs/2102.09690) (Feb 2021)

  - [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://arxiv.org/abs/2101.00190) (Jan 2021)

  - [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://arxiv.org/abs/2010.15980) (Oct 2020)

  - [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) (May 2020)

  - [How Can We Know What Language Models Know?](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00324/96460/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](https://arxiv.org/abs/2302.10916) (Feb 2023) 

  - [Grimm in Wonderland: Prompt Engineering with Midjourney to Illustrate Fairytales](https://arxiv.org/abs/2302.08961) (Feb 2023)

  - [LabelPrompt: Effective Prompt-based Learning for Relation Classification](https://arxiv.org/abs/2302.08068) (Feb 2023)

  - [Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition](https://arxiv.org/abs/2302.08102) (Feb 2023)

  - [The Capacity for Moral Self-Correction in Large Language Models](https://arxiv.org/abs/2302.07459) (Feb 2023)

  - [Prompting for Multimodal Hateful Meme Classification](https://arxiv.org/abs/2302.04156) (Feb 2023)

  - [PLACES: Prompting Language Models for Social Conversation Synthesis](https://arxiv.org/abs/2302.03269) (Feb 2023)

  - [Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation](https://arxiv.org/abs/2302.01441) (Feb 2023)

  - [Crawling the Internal Knowledge-Base of Language Models](https://arxiv.org/abs/2301.12810) (Jan 2023)

  - [Legal Prompt Engineering for Multilingual Legal Judgement Prediction](https://arxiv.org/abs/2212.02199) (Dec 2022)

  - [Investigating Prompt Engineering in Diffusion Models](https://arxiv.org/abs/2211.15462) (Nov 2022)

  - [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://arxiv.org/abs/2209.09513v2) (Sep 2022)

  - [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://arxiv.org/abs/2210.15157) (Oct 2022)

  - [Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic?](https://arxiv.org/abs/2210.14699) (Oct 2022)

  - [Plot Writing From Scratch Pre-Trained Language Models](https://aclanthology.org/2022.inlg-main.5) (July 2022)

- Collections:

  - [Chain-of-ThoughtsPapers](https://github.com/Timothyxxx/Chain-of-ThoughtsPapers)

  - [Papers with Code](https://paperswithcode.com/task/prompt-engineering)

  - [Prompt Papers](https://github.com/thunlp/PromptPapers#papers)

---

## 工具和库

#### (Sorted by Name)

- [AI Test Kitchen](https://aitestkitchen.withgoogle.com)

- [betterprompt](https://github.com/krrishdholakia/betterprompt)

- [DreamStudio](https://beta.dreamstudio.ai)

- [DUST](https://dust.tt)

- [Dyno](https://trydyno.com)

- [EveryPrompt](https://www.everyprompt.com)

- [GPT Index](https://github.com/jerryjliu/gpt_index)

- [GPTTools](https://gpttools.com/comparisontool)

- [hwchase17/adversarial-prompts](https://github.com/hwchase17/adversarial-prompts)

- [Interactive Composition Explorer](https://github.com/oughtinc/ice)

- [LangChain](https://github.com/hwchase17/langchain)

- [LearnGPT](https://www.learngpt.com)

- [Lexica](https://lexica.art)

- [loom](https://github.com/socketteer/loom)

- [Metaprompt](https://metaprompt.vercel.app/?task=gpt)

- [OpenAI Playground](https://beta.openai.com/playground)

- [OpenPrompt](https://github.com/thunlp/OpenPrompt)

- [Playground](https://playgroundai.com)

- [Prodia](https://app.prodia.com/#/)

- [Prompt Base](https://promptbase.com)

- [Prompt Engine](https://github.com/microsoft/prompt-engine)

- [Prompt Generator for OpenAI's DALL-E 2](http://dalle2-prompt-generator.s3-website-us-west-2.amazonaws.com)

- [Promptable](https://promptable.ai)

- [PromptInject](https://github.com/agencyenterprise/PromptInject)

- [Prompts.ai](https://github.com/sevazhidkov/prompts-ai)

- [Promptly](https://trypromptly.com/)

- [PromptSource](https://github.com/bigscience-workshop/promptsource)

- [Promptist](https://promptist.herokuapp.com/)

- [Scale SpellBook](https://scale.com/spellbook)

- [sharegpt](https://sharegpt.com)

- [ThoughtSource](https://github.com/OpenBioLink/ThoughtSource)

- [Visual Prompt Builder](https://tools.saxifrage.xyz/prompt)

---

## Datasets

#### (Sorted by Name)

- [Anthropic's Red Team dataset](https://github.com/anthropics/hh-rlhf/tree/master/red-team-attempts), [(paper)](https://arxiv.org/abs/2209.07858)

- [Awesome ChatGPT Prompts](https://huggingface.co/datasets/fka/awesome-chatgpt-prompts)

- [DiffusionDB](https://github.com/poloclub/diffusiondb)

- [Midjourney Prompts](https://huggingface.co/datasets/succinctly/midjourney-prompts)

- [P3 - Public Pool of Prompts](https://huggingface.co/datasets/bigscience/P3)

- [PartiPrompts](https://parti.research.google)

- [Real Toxicity Prompts](https://allenai.org/data/real-toxicity-prompts)

- [Stable Diffusion Dataset](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts)

- [WritingPrompts](https://www.reddit.com/r/WritingPrompts)

---

## Blog, Guides, Tutorials and Other Readings

#### (Sorted by Name)

- [3 Principles for prompt engineering with GPT-3](https://www.linkedin.com/pulse/3-principles-prompt-engineering-gpt-3-ben-whately)

- [A beginner-friendly guide to generative language models - LaMBDA guide](https://aitestkitchen.withgoogle.com/how-lamda-works)

- [A Complete Introduction to Prompt Engineering for Large Language Models](https://www.mihaileric.com/posts/a-complete-introduction-to-prompt-engineering)

- [A Generic Framework for ChatGPT Prompt Engineering](https://medium.com/@thorbjoern.heise/a-generic-framework-for-chatgpt-prompt-engineering-7097f6513a0b)

- [AI Content Generation](https://www.jonstokes.com/p/ai-content-generation-part-1-machine)

- [AI's rise generates new job title: Prompt engineer](https://www.axios.com/2023/02/22/chatgpt-prompt-engineers-ai-job)

- [Awesome ChatGPT Prompts](https://github.com/f/awesome-chatgpt-prompts)

- [Best 100+ Stable Diffusion Prompts](https://mpost.io/best-100-stable-diffusion-prompts-the-most-beautiful-ai-text-to-image-prompts)

- [Best practices for prompt engineering with OpenAI API](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api)

- [Building GPT-3 applications — beyond the prompt](https://medium.com/data-science-at-microsoft/building-gpt-3-applications-beyond-the-prompt-504140835560)

- [ChatGPT, AI and GPT-3 Apps and use cases](https://gpt3demo.com)

- [CMU Advanced NLP 2022: Prompting](https://youtube.com/watch?v=5ef83Wljm-M&feature=shares)

- [Curtis64's set of prompt gists](https://gist.github.com/Curtis-64)

- [DALL·E 2 Prompt Engineering Guide](https://docs.google.com/document/d/11WlzjBT0xRpQhP9tFMtxzd0q6ANIdHPUBkMV-YB043U/edit#)

- [DALL·E 2 Preview - Risks and Limitations](https://github.com/openai/dalle-2-preview/blob/main/system-card.md)

- [DALLE Prompt Book](https://dallery.gallery/the-dalle-2-prompt-book)

- [DALL-E, Make Me Another Picasso, Please](https://www.newyorker.com/magazine/2022/07/11/dall-e-make-me-another-picasso-please?)

- [Diffusion Models: A Practical Guide](https://scale.com/guides/diffusion-models-guide)

- [Exploiting GPT-3 Prompts](https://twitter.com/goodside/status/1569128808308957185)

- [Exploring Prompt Injection Attacks](https://research.nccgroup.com/2022/12/05/exploring-prompt-injection-attacks)

- [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)

- [Generative AI with Cohere: Part 1 - Model Prompting](https://txt.cohere.ai/generative-ai-part-1)

- [Giving GPT-3 a Turing Test](https://lacker.io/ai/2020/07/06/giving-gpt-3-a-turing-test.html)

- [GPT-3 & Beyond](https://youtube.com/watch?v=-lnHHWRCDGk)

- [GPT3 and Prompts: A quick primer](https://buildspace.so/notes/intro-to-gpt3-prompts)

- [How to Draw Anything](https://andys.page/posts/how-to-draw)

- [How to get images that don't suck](https://www.reddit.com/r/StableDiffusion/comments/x41n87/how_to_get_images_that_dont_suck_a)

- [How to make LLMs say true things](https://evanjconrad.com/posts/world-models)

- [How to write good prompts](https://andymatuschak.org/prompts)

- [Introduction to Reinforcement Learning with Human Feedback](https://www.surgehq.ai/blog/introduction-to-reinforcement-learning-with-human-feedback-rlhf-series-part-1)

- [In defense of prompt engineering](https://simonwillison.net/2023/Feb/21/in-defense-of-prompt-engineering/)

- [Language Models and Prompt Engineering: Systematic Survey of Prompting Methods in NLP](https://youtube.com/watch?v=OsbUfL8w-mo&feature=shares)

- [Learn Prompting](https://learnprompting.org)

- [Methods of prompt programming](https://generative.ink/posts/methods-of-prompt-programming)

- [Mysteries of mode collapse](https://www.lesswrong.com/posts/t9svvNPNmFf5Qa3TA/mysteries-of-mode-collapse)

- [NLP for Text-to-Image Generators: Prompt Analysis](https://heartbeat.comet.ml/nlp-for-text-to-image-generators-prompt-analysis-part-1-5076a44d8365)

- [NLP with Deep Learning CS224N/Ling284 - Lecture 11: Promting, Instruction Tuning, and RLHF](http://web.stanford.edu/class/cs224n/slides/cs224n-2023-lecture11-prompting-rlhf.pdf)

- [Notes for Prompt Engineering by sw-yx](https://github.com/sw-yx/ai-notes)

- [OpenAI Cookbook](https://github.com/openai/openai-cookbook)

- [OpenAI Prompt Examples for several applications](https://platform.openai.com/examples)

- [Pretrain, Prompt, Predict -  A New Paradigm for NLP](http://pretrain.nlpedia.ai)

- [Prompt Engineering 101 - Introduction and resources](https://www.linkedin.com/pulse/prompt-engineering-101-introduction-resources-amatriain)

- [Prompt Engineering 101: Autocomplete, Zero-shot, One-shot, and Few-shot prompting](https://youtube.com/watch?v=v2gD8BHOaX4&feature=shares)

- [Prompt Engineering 101](https://humanloop.com/blog/prompt-engineering-101)

- [Prompt Engineering - A new profession ?](https://www.youtube.com/watch?v=w102J3_9Bcs&ab_channel=PatrickDebois)

- [Prompt Engineering by co:here](https://docs.cohere.ai/docs/prompt-engineering)

- [Prompt Engineering by Microsoft](https://microsoft.github.io/prompt-engineering)

- [Prompt Engineering: The Career of Future](https://shubhamsaboo111.medium.com/prompt-engineering-the-career-of-future-2fb93f90f117)

- [Prompt engineering davinci-003 on our own docs for automated support (Part I)](https://www.patterns.app/blog/2022/12/21/finetune-llm-tech-support)

- [Prompt Engineering Guide: How to Engineer the Perfect Prompts](https://richardbatt.co.uk/prompt-engineering-guide-how-to-engineer-the-perfect-prompts)

- [Prompt Engineering in GPT-3](https://www.analyticsvidhya.com/blog/2022/05/prompt-engineering-in-gpt-3)

- [Prompt Engineering Template](https://docs.google.com/spreadsheets/d/1-snKDn38-KypoYCk9XLPg799bHcNFSBAVu2HVvFEAkA/edit#gid=0)

- [Prompt Engineering Topic by GitHub](https://github.com/topics/prompt-engineering)

- [Prompt Engineering: From Words to Art](https://www.saxifrage.xyz/post/prompt-engineering)

- [Prompt Engineering with OpenAI's GPT-3 and other LLMs](https://youtube.com/watch?v=BP9fi_0XTlw&feature=shares)

- [Prompt injection attacks against GPT-3](https://simonwillison.net/2022/Sep/12/prompt-injection)

- [Prompt injection to read out the secret OpenAI API key](https://twitter.com/ludwig_stumpp/status/1619701277419794435?s=20&t=GtoMlmYCSt-UmvjqJVbBSA)

- [Prompting in NLP: Prompt-based zero-shot learning](https://savasy-22028.medium.com/prompting-in-nlp-prompt-based-zero-shot-learning-3f34bfdb2b72)

- [Prompting Methods with Language Models and Their Applications to Weak Supervision](https://snorkel.ai/prompting-methods-with-language-models-nlp)

- [Prompts as Programming by Gwern](https://www.gwern.net/GPT-3#prompts-as-programming)

- [Reverse Prompt Engineering for Fun and (no) Profit](https://lspace.swyx.io/p/reverse-prompt-eng)

- [So you want to be a prompt engineer: Critical careers of the future](https://venturebeat.com/ai/so-you-want-to-be-a-prompt-engineer-critical-careers-of-the-future/)

- [Simulators](https://www.lesswrong.com/posts/vJFdjigzmcXMhNTsx/simulators)

- [Start with an Instruction](https://beta.openai.com/docs/quickstart/start-with-an-instruction)

- [Talking to machines: prompt engineering & injection](https://artifact-research.com/artificial-intelligence/talking-to-machines-prompt-engineering-injection)

- [The Book - Fed Honeypot](https://fedhoneypot.notion.site/25fdbdb69e9e44c6877d79e18336fe05?v=1d2bf4143680451986fd2836a04afbf4)

- [The ChatGPT Prompt Book](https://docs.google.com/presentation/d/17b_ocq-GL5lhV_bYSShzUgxL02mtWDoiw9xEroJ5m3Q/edit#slide=id.gc6f83aa91_0_79)

- [The Mirror of Language](https://deepfates.com/the-mirror-of-language)

- [Using GPT-Eliezer against ChatGPT Jailbreaking](https://www.alignmentforum.org/posts/pNcFYZnPdXyL2RfgA/using-gpt-eliezer-against-chatgpt-jailbreaking)

- [What Is ChatGPT Doing … and Why Does It Work?](https://writings.stephenwolfram.com/2023/02/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!