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Prompt Engineering - Introductionhttps://patch-diff.githubusercontent.com/few/Prompt-Engineering-Guide/blob/main/guides/prompts-intro.md
Prompt Engineering - Basic Promptinghttps://patch-diff.githubusercontent.com/few/Prompt-Engineering-Guide/blob/main/guides/prompts-basic-usage.md
Prompt Engineering - Advanced Promptinghttps://patch-diff.githubusercontent.com/few/Prompt-Engineering-Guide/blob/main/guides/prompts-advanced-usage.md
Prompt Engineering - Adversarial Promptinghttps://patch-diff.githubusercontent.com/few/Prompt-Engineering-Guide/blob/main/guides/prompt-adversarial.md
Prompt Engineering - Miscellaneous Topicshttps://patch-diff.githubusercontent.com/few/Prompt-Engineering-Guide/blob/main/guides/prompt-miscellaneous.md
https://patch-diff.githubusercontent.com/few/Prompt-Engineering-Guide#papers
Augmented Language Models: a Surveyhttps://arxiv.org/abs/2302.07842
A Survey for In-context Learninghttps://arxiv.org/abs/2301.00234
Towards Reasoning in Large Language Models: A Surveyhttps://arxiv.org/abs/2212.10403
Emergent Abilities of Large Language Modelshttps://arxiv.org/abs/2206.07682
A Taxonomy of Prompt Modifiers for Text-To-Image Generationhttps://arxiv.org/abs/2204.13988
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processinghttps://arxiv.org/abs/2107.13586
Active Prompting with Chain-of-Thought for Large Language Modelshttps://arxiv.org/abs/2302.12246
More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Modelshttps://arxiv.org/abs/2302.12173
Guiding Large Language Models via Directional Stimulus Promptinghttps://arxiv.org/abs/2302.11520
How Does In-Context Learning Help Prompt Tuning?https://arxiv.org/abs/2302.11521
Scalable Prompt Generation for Semi-supervised Learning with Language Modelshttps://arxiv.org/abs/2302.09236
Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraintshttps://arxiv.org/abs/2302.09185
À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Promptinghttps://arxiv.org/abs/2302.07994
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networkshttps://arxiv.org/abs/2302.08043
The Capacity for Moral Self-Correction in Large Language Modelshttps://arxiv.org/abs/2302.07459
SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domainshttps://arxiv.org/abs/2302.06868
Evaluating the Robustness of Discrete Promptshttps://arxiv.org/abs/2302.05619
Compositional Exemplars for In-context Learninghttps://arxiv.org/abs/2302.05698
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discoveryhttps://arxiv.org/abs/2302.03668
Multimodal Chain-of-Thought Reasoning in Language Modelshttps://arxiv.org/abs/2302.00923
Large Language Models Can Be Easily Distracted by Irrelevant Contexthttps://arxiv.org/abs/2302.00093
Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Modelshttps://arxiv.org/abs/2302.00618
Progressive Prompts: Continual Learning for Language Modelshttps://arxiv.org/abs/2301.12314
Batch Prompting: Efficient Inference with LLM APIshttps://arxiv.org/abs/2301.08721
On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoninghttps://arxiv.org/abs/2212.08061
Constitutional AI: Harmlessness from AI Feedbackhttps://arxiv.org/abs/2212.08073
Successive Prompting for Decomposing Complex Questionshttps://arxiv.org/abs/2212.04092
Discovering Language Model Behaviors with Model-Written Evaluationshttps://arxiv.org/abs/2212.09251
Structured Prompting: Scaling In-Context Learning to 1,000 Exampleshttps://arxiv.org/abs/2212.06713
PAL: Program-aided Language Modelshttps://arxiv.org/abs/2211.10435
Large Language Models Are Human-Level Prompt Engineershttps://arxiv.org/abs/2211.01910
Ignore Previous Prompt: Attack Techniques For Language Modelshttps://arxiv.org/abs/2211.09527
Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methodshttps://arxiv.org/abs/2210.07321
Teaching Algorithmic Reasoning via In-context Learninghttps://arxiv.org/abs/2211.09066
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inferencehttps://arxiv.org/abs/2211.11875
Ask Me Anything: A simple strategy for prompting language modelshttps://paperswithcode.com/paper/ask-me-anything-a-simple-strategy-for
ReAct: Synergizing Reasoning and Acting in Language Modelshttps://arxiv.org/abs/2210.03629
Prompting GPT-3 To Be Reliablehttps://arxiv.org/abs/2210.09150
Decomposed Prompting: A Modular Approach for Solving Complex Taskshttps://arxiv.org/abs/2210.02406
Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thoughthttps://arxiv.org/abs/2210.01240v3
Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Exampleshttps://arxiv.org/abs/2209.02128
Promptagator: Few-shot Dense Retrieval From 8 Exampleshttps://arxiv.org/abs/2209.11755
On the Advance of Making Language Models Better Reasonershttps://arxiv.org/abs/2206.02336
Large Language Models are Zero-Shot Reasonershttps://arxiv.org/abs/2205.11916
MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoninghttps://arxiv.org/abs/2205.00445
Toxicity Detection with Generative Prompt-based Inferencehttps://arxiv.org/abs/2205.12390
Learning to Transfer Prompts for Text Generationhttps://arxiv.org/abs/2205.01543
The Unreliability of Explanations in Few-shot Prompting for Textual Reasoninghttps://arxiv.org/abs/2205.03401
A Taxonomy of Prompt Modifiers for Text-To-Image Generationhttps://arxiv.org/abs/2204.13988
PromptChainer: Chaining Large Language Model Prompts through Visual Programminghttps://arxiv.org/abs/2203.06566
Self-Consistency Improves Chain of Thought Reasoning in Language Modelshttps://arxiv.org/abs/2203.11171
Training language models to follow instructions with human feedbackhttps://arxiv.org/abs/2203.02155
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?https://arxiv.org/abs/2202.12837
Chain of Thought Prompting Elicits Reasoning in Large Language Modelshttps://arxiv.org/abs/2201.11903
Show Your Work: Scratchpads for Intermediate Computation with Language Modelshttps://arxiv.org/abs/2112.00114
Generated Knowledge Prompting for Commonsense Reasoninghttps://arxiv.org/abs/2110.08387
Multitask Prompted Training Enables Zero-Shot Task Generalizationhttps://arxiv.org/abs/2110.08207
Reframing Instructional Prompts to GPTk's Languagehttps://arxiv.org/abs/2109.07830
Design Guidelines for Prompt Engineering Text-to-Image Generative Modelshttps://arxiv.org/abs/2109.06977
Making Pre-trained Language Models Better Few-shot Learnershttps://aclanthology.org/2021.acl-long.295
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivityhttps://arxiv.org/abs/2104.08786
BERTese: Learning to Speak to BERThttps://aclanthology.org/2021.eacl-main.316
The Power of Scale for Parameter-Efficient Prompt Tuninghttps://arxiv.org/abs/2104.08691
Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigmhttps://arxiv.org/abs/2102.07350
Calibrate Before Use: Improving Few-Shot Performance of Language Modelshttps://arxiv.org/abs/2102.09690
Prefix-Tuning: Optimizing Continuous Prompts for Generationhttps://arxiv.org/abs/2101.00190
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Promptshttps://arxiv.org/abs/2010.15980
Language Models are Few-Shot Learnershttps://arxiv.org/abs/2005.14165
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
How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Studyhttps://arxiv.org/abs/2302.10916
Grimm in Wonderland: Prompt Engineering with Midjourney to Illustrate Fairytaleshttps://arxiv.org/abs/2302.08961
LabelPrompt: Effective Prompt-based Learning for Relation Classificationhttps://arxiv.org/abs/2302.08068
Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognitionhttps://arxiv.org/abs/2302.08102
The Capacity for Moral Self-Correction in Large Language Modelshttps://arxiv.org/abs/2302.07459
Prompting for Multimodal Hateful Meme Classificationhttps://arxiv.org/abs/2302.04156
PLACES: Prompting Language Models for Social Conversation Synthesishttps://arxiv.org/abs/2302.03269
Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generationhttps://arxiv.org/abs/2302.01441
Crawling the Internal Knowledge-Base of Language Modelshttps://arxiv.org/abs/2301.12810
Legal Prompt Engineering for Multilingual Legal Judgement Predictionhttps://arxiv.org/abs/2212.02199
Investigating Prompt Engineering in Diffusion Modelshttps://arxiv.org/abs/2211.15462
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answeringhttps://arxiv.org/abs/2209.09513v2
Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Languagehttps://arxiv.org/abs/2210.15157
Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic?https://arxiv.org/abs/2210.14699
Plot Writing From Scratch Pre-Trained Language Modelshttps://aclanthology.org/2022.inlg-main.5
Chain-of-ThoughtsPapershttps://github.com/Timothyxxx/Chain-of-ThoughtsPapers
Papers with Codehttps://paperswithcode.com/task/prompt-engineering
Prompt Papershttps://github.com/thunlp/PromptPapers#papers
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AI Test Kitchenhttps://aitestkitchen.withgoogle.com
betterprompthttps://github.com/krrishdholakia/betterprompt
DreamStudiohttps://beta.dreamstudio.ai
DUSThttps://dust.tt
Dynohttps://trydyno.com
EveryPrompthttps://www.everyprompt.com
GPT Indexhttps://github.com/jerryjliu/gpt_index
GPTToolshttps://gpttools.com/comparisontool
hwchase17/adversarial-promptshttps://github.com/hwchase17/adversarial-prompts
Interactive Composition Explorerhttps://github.com/oughtinc/ice
LangChainhttps://github.com/hwchase17/langchain
LearnGPThttps://www.learngpt.com
Lexicahttps://lexica.art
loomhttps://github.com/socketteer/loom
Metaprompthttps://metaprompt.vercel.app/?task=gpt
OpenAI Playgroundhttps://beta.openai.com/playground
OpenPrompthttps://github.com/thunlp/OpenPrompt
Playgroundhttps://playgroundai.com
Prodiahttps://app.prodia.com/#/
Prompt Basehttps://promptbase.com
Prompt Enginehttps://github.com/microsoft/prompt-engine
Prompt Generator for OpenAI's DALL-E 2http://dalle2-prompt-generator.s3-website-us-west-2.amazonaws.com
Promptablehttps://promptable.ai
PromptInjecthttps://github.com/agencyenterprise/PromptInject
Prompts.aihttps://github.com/sevazhidkov/prompts-ai
Promptlyhttps://trypromptly.com/
PromptSourcehttps://github.com/bigscience-workshop/promptsource
Promptisthttps://promptist.herokuapp.com/
Scale SpellBookhttps://scale.com/spellbook
sharegpthttps://sharegpt.com
ThoughtSourcehttps://github.com/OpenBioLink/ThoughtSource
Visual Prompt Builderhttps://tools.saxifrage.xyz/prompt
https://patch-diff.githubusercontent.com/few/Prompt-Engineering-Guide#datasets
https://patch-diff.githubusercontent.com/few/Prompt-Engineering-Guide#sorted-by-name-1
Anthropic's Red Team datasethttps://github.com/anthropics/hh-rlhf/tree/master/red-team-attempts
(paper)https://arxiv.org/abs/2209.07858
Awesome ChatGPT Promptshttps://huggingface.co/datasets/fka/awesome-chatgpt-prompts
DiffusionDBhttps://github.com/poloclub/diffusiondb
Midjourney Promptshttps://huggingface.co/datasets/succinctly/midjourney-prompts
P3 - Public Pool of Promptshttps://huggingface.co/datasets/bigscience/P3
PartiPromptshttps://parti.research.google
Real Toxicity Promptshttps://allenai.org/data/real-toxicity-prompts
Stable Diffusion Datasethttps://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts
WritingPromptshttps://www.reddit.com/r/WritingPrompts
https://patch-diff.githubusercontent.com/few/Prompt-Engineering-Guide#blog-guides-tutorials-and-other-readings
https://patch-diff.githubusercontent.com/few/Prompt-Engineering-Guide#sorted-by-name-2
3 Principles for prompt engineering with GPT-3https://www.linkedin.com/pulse/3-principles-prompt-engineering-gpt-3-ben-whately
A beginner-friendly guide to generative language models - LaMBDA guidehttps://aitestkitchen.withgoogle.com/how-lamda-works
A Complete Introduction to Prompt Engineering for Large Language Modelshttps://www.mihaileric.com/posts/a-complete-introduction-to-prompt-engineering
A Generic Framework for ChatGPT Prompt Engineeringhttps://medium.com/@thorbjoern.heise/a-generic-framework-for-chatgpt-prompt-engineering-7097f6513a0b
AI Content Generationhttps://www.jonstokes.com/p/ai-content-generation-part-1-machine
AI's rise generates new job title: Prompt engineerhttps://www.axios.com/2023/02/22/chatgpt-prompt-engineers-ai-job
Awesome ChatGPT Promptshttps://github.com/f/awesome-chatgpt-prompts
Best 100+ Stable Diffusion Promptshttps://mpost.io/best-100-stable-diffusion-prompts-the-most-beautiful-ai-text-to-image-prompts
Best practices for prompt engineering with OpenAI APIhttps://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api
Building GPT-3 applications — beyond the prompthttps://medium.com/data-science-at-microsoft/building-gpt-3-applications-beyond-the-prompt-504140835560
ChatGPT, AI and GPT-3 Apps and use caseshttps://gpt3demo.com
CMU Advanced NLP 2022: Promptinghttps://youtube.com/watch?v=5ef83Wljm-M&feature=shares
Curtis64's set of prompt gistshttps://gist.github.com/Curtis-64
DALL·E 2 Prompt Engineering Guidehttps://docs.google.com/document/d/11WlzjBT0xRpQhP9tFMtxzd0q6ANIdHPUBkMV-YB043U/edit
DALL·E 2 Preview - Risks and Limitationshttps://github.com/openai/dalle-2-preview/blob/main/system-card.md
DALLE Prompt Bookhttps://dallery.gallery/the-dalle-2-prompt-book
DALL-E, Make Me Another Picasso, Pleasehttps://www.newyorker.com/magazine/2022/07/11/dall-e-make-me-another-picasso-please
Diffusion Models: A Practical Guidehttps://scale.com/guides/diffusion-models-guide
Exploiting GPT-3 Promptshttps://twitter.com/goodside/status/1569128808308957185
Exploring Prompt Injection Attackshttps://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 Mysterioushttp://ai.stanford.edu/blog/in-context-learning
Generative AI with Cohere: Part 1 - Model Promptinghttps://txt.cohere.ai/generative-ai-part-1
Giving GPT-3 a Turing Testhttps://lacker.io/ai/2020/07/06/giving-gpt-3-a-turing-test.html
GPT3 and Prompts: A quick primerhttps://buildspace.so/notes/intro-to-gpt3-prompts
How to Draw Anythinghttps://andys.page/posts/how-to-draw
How to get images that don't suckhttps://www.reddit.com/r/StableDiffusion/comments/x41n87/how_to_get_images_that_dont_suck_a
How to make LLMs say true thingshttps://evanjconrad.com/posts/world-models
How to write good promptshttps://andymatuschak.org/prompts
Introduction to Reinforcement Learning with Human Feedbackhttps://www.surgehq.ai/blog/introduction-to-reinforcement-learning-with-human-feedback-rlhf-series-part-1
In defense of prompt engineeringhttps://simonwillison.net/2023/Feb/21/in-defense-of-prompt-engineering/
Language Models and Prompt Engineering: Systematic Survey of Prompting Methods in NLPhttps://youtube.com/watch?v=OsbUfL8w-mo&feature=shares
Learn Promptinghttps://learnprompting.org
Methods of prompt programminghttps://generative.ink/posts/methods-of-prompt-programming
Mysteries of mode collapsehttps://www.lesswrong.com/posts/t9svvNPNmFf5Qa3TA/mysteries-of-mode-collapse
NLP for Text-to-Image Generators: Prompt Analysishttps://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 RLHFhttp://web.stanford.edu/class/cs224n/slides/cs224n-2023-lecture11-prompting-rlhf.pdf
Notes for Prompt Engineering by sw-yxhttps://github.com/sw-yx/ai-notes
OpenAI Cookbookhttps://github.com/openai/openai-cookbook
OpenAI Prompt Examples for several applicationshttps://platform.openai.com/examples
Pretrain, Prompt, Predict - A New Paradigm for NLPhttp://pretrain.nlpedia.ai
Prompt Engineering 101 - Introduction and resourceshttps://www.linkedin.com/pulse/prompt-engineering-101-introduction-resources-amatriain
Prompt Engineering 101: Autocomplete, Zero-shot, One-shot, and Few-shot promptinghttps://youtube.com/watch?v=v2gD8BHOaX4&feature=shares
Prompt Engineering 101https://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:herehttps://docs.cohere.ai/docs/prompt-engineering
Prompt Engineering by Microsofthttps://microsoft.github.io/prompt-engineering
Prompt Engineering: The Career of Futurehttps://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 Promptshttps://richardbatt.co.uk/prompt-engineering-guide-how-to-engineer-the-perfect-prompts
Prompt Engineering in GPT-3https://www.analyticsvidhya.com/blog/2022/05/prompt-engineering-in-gpt-3
Prompt Engineering Templatehttps://docs.google.com/spreadsheets/d/1-snKDn38-KypoYCk9XLPg799bHcNFSBAVu2HVvFEAkA/edit#gid=0
Prompt Engineering Topic by GitHubhttps://github.com/topics/prompt-engineering
Prompt Engineering: From Words to Arthttps://www.saxifrage.xyz/post/prompt-engineering
Prompt Engineering with OpenAI's GPT-3 and other LLMshttps://youtube.com/watch?v=BP9fi_0XTlw&feature=shares
Prompt injection attacks against GPT-3https://simonwillison.net/2022/Sep/12/prompt-injection
Prompt injection to read out the secret OpenAI API keyhttps://twitter.com/ludwig_stumpp/status/1619701277419794435?s=20&t=GtoMlmYCSt-UmvjqJVbBSA
Prompting in NLP: Prompt-based zero-shot learninghttps://savasy-22028.medium.com/prompting-in-nlp-prompt-based-zero-shot-learning-3f34bfdb2b72
Prompting Methods with Language Models and Their Applications to Weak Supervisionhttps://snorkel.ai/prompting-methods-with-language-models-nlp
Prompts as Programming by Gwernhttps://www.gwern.net/GPT-3#prompts-as-programming
Reverse Prompt Engineering for Fun and (no) Profithttps://lspace.swyx.io/p/reverse-prompt-eng
So you want to be a prompt engineer: Critical careers of the futurehttps://venturebeat.com/ai/so-you-want-to-be-a-prompt-engineer-critical-careers-of-the-future/
Simulatorshttps://www.lesswrong.com/posts/vJFdjigzmcXMhNTsx/simulators
Start with an Instructionhttps://beta.openai.com/docs/quickstart/start-with-an-instruction
Talking to machines: prompt engineering & injectionhttps://artifact-research.com/artificial-intelligence/talking-to-machines-prompt-engineering-injection
The Book - Fed Honeypothttps://fedhoneypot.notion.site/25fdbdb69e9e44c6877d79e18336fe05?v=1d2bf4143680451986fd2836a04afbf4
The ChatGPT Prompt Bookhttps://docs.google.com/presentation/d/17b_ocq-GL5lhV_bYSShzUgxL02mtWDoiw9xEroJ5m3Q/edit#slide=id.gc6f83aa91_0_79
The Mirror of Languagehttps://deepfates.com/the-mirror-of-language
Using GPT-Eliezer against ChatGPT Jailbreakinghttps://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/
Readme https://patch-diff.githubusercontent.com/few/Prompt-Engineering-Guide#readme-ov-file
MIT license https://patch-diff.githubusercontent.com/few/Prompt-Engineering-Guide#MIT-1-ov-file
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