Maintained by Fourth Industrial Systems Corporation (4th.is) — an enterprise-ready, curated catalog of research, tools, datasets, models, and learning paths for Prompt Engineering across LLMs (GPT, ChatGPT, PaLM, and more).

• Mission
• Papers
  • Prompt Engineering Techniques
  • Reasoning & In-Context Learning
  • Evaluating & Improving LMs
  • Applications
  • Threat Detection & Adversarial
  • Few-shot & Optimization
  • Text→Image
  • Text→Music/Sound
  • Text→Video
  • Overviews
• Tools & Code
• APIs
• Datasets
• Models
• AI Content Detectors
• Educational
  • Courses
  • Tutorials
• Videos
• Communities
• Contribution
• License

At Fourth Industrial Systems, we view Prompt Engineering as a critical discipline for the Fourth Industrial Revolution. This repository consolidates leading research, code, and practices to empower enterprises, researchers, and practitioners with scalable, ethical, and innovative AI methods.

• A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT — https://arxiv.org/abs/2302.11382
• Ask Me Anything: A simple strategy for prompting language models — https://paperswithcode.com/paper/ask-me-anything-a-simple-strategy-for
• Batch Prompting: Efficient Inference with LLM APIs — https://arxiv.org/abs/2301.08721
• Decomposed Prompting: A Modular Approach for Solving Complex Tasks — https://arxiv.org/abs/2210.02406
• Fantastically Ordered Prompts and Where to Find Them — https://arxiv.org/abs/2104.08786
• Hard Prompts Made Easy — https://arxiv.org/abs/2302.03668
• Investigating Prompt Engineering in Diffusion Models — https://arxiv.org/abs/2211.15462
• Prefix-Tuning: Optimizing Continuous Prompts for Generation — https://arxiv.org/abs/2101.00190
• Prompt Programming for Large Language Models — https://arxiv.org/abs/2102.07350
• PromptChainer: Chaining Large Language Model Prompts — https://arxiv.org/abs/2203.06566
• Prompting GPT-3 To Be Reliable — https://arxiv.org/abs/2210.09150
• Progressive Prompts: Continual Learning for Language Models — https://arxiv.org/abs/2301.12314
• Reframing Instructional Prompts to GPTk’s Language — https://arxiv.org/abs/2109.07830
• Show Your Work: Scratchpads for Intermediate Computation — https://arxiv.org/abs/2112.00114
• Structured Prompting: Scaling In-Context Learning to 1,000 Examples — https://arxiv.org/abs/2212.06713
• Successive Prompting for Decompleting Complex Questions — https://arxiv.org/abs/2212.04092
• Text Mining for Prompt Engineering — https://aclanthology.org/2023.findings-acl.709.pdf
• The Power of Scale for Parameter-Efficient Prompt Tuning — https://arxiv.org/abs/2104.08691

• BERTese: Learning to Speak to BERT — https://aclanthology.org/2021.eacl-main.316
• Chain of Thought Prompting Elicits Reasoning — https://arxiv.org/abs/2201.11903
• Generated Knowledge Prompting for Commonsense Reasoning — https://arxiv.org/abs/2110.08387
• Learn to Explain: Multimodal Reasoning via Thought Chains — https://arxiv.org/abs/2209.09513v2
• Large Language Models are Zero-Shot Reasoners — https://arxiv.org/abs/2205.11916
• Language Models Are Greedy Reasoners — https://arxiv.org/abs/2210.01240v3
• Multimodal Chain-of-Thought Reasoning — https://arxiv.org/abs/2302.00923
• On Second Thought, Let’s Not Think Step by Step — https://arxiv.org/abs/2212.08061
• On the Advance of Making Language Models Better Reasoners — https://arxiv.org/abs/2206.02336
• ReAct: Synergizing Reasoning and Acting — https://arxiv.org/abs/2210.03629
• Rethinking the Role of Demonstrations — https://arxiv.org/abs/2202.12837
• Self-Consistency Improves Chain of Thought — https://arxiv.org/abs/2203.11171

• Calibrate Before Use — https://arxiv.org/abs/2102.09690
• Crawling the Internal Knowledge-Base of Language Models — https://arxiv.org/abs/2301.12810
• Discovering Language Model Behaviors with Model-Written Evaluations — https://arxiv.org/abs/2212.09251
• Large Language Models Can Be Easily Distracted by Irrelevant Context — https://arxiv.org/abs/2302.00093

• AutoPrompt — https://arxiv.org/abs/2010.15980
• Commonsense-Aware Prompting for Controllable Empathetic Dialogue — https://arxiv.org/abs/2302.01441
• Conversing with Copilot — https://arxiv.org/abs/2210.15157
• Legal Prompt Engineering for Multilingual Legal Judgement Prediction — https://arxiv.org/abs/2212.02199
• PAL: Program-aided Language Models — https://arxiv.org/abs/2211.10435
• PLACES: Prompting Language Models for Social Conversation Synthesis — https://arxiv.org/abs/2302.03269
• Plot Writing From Scratch — https://aclanthology.org/2022.inlg-main.5
• Prompting for Multimodal Hateful Meme Classification — https://arxiv.org/abs/2302.04156
• Rephrase and Respond — https://arxiv.org/abs/2311.04205

• Constitutional AI — https://arxiv.org/abs/2212.08073
• Evaluating the Susceptibility via Handcrafted Adversarial Examples — https://arxiv.org/abs/2209.02128
• 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
• Ignore Previous Prompt — https://arxiv.org/abs/2211.09527
• Machine Generated Text: Survey of Threat Models and Detection — https://arxiv.org/abs/2210.07321
• Toxicity Detection with Generative Prompt-based Inference — https://arxiv.org/abs/2205.12390

• Language Models are Few-Shot Learners — https://arxiv.org/abs/2005.14165
• Making Pre-trained LMs Better Few-shot Learners — https://aclanthology.org/2021.acl-long.295
• Promptagator — https://arxiv.org/abs/2209.11755
• Unreliability of Explanations in Few-shot Prompting — https://arxiv.org/abs/2205.03401

• A Taxonomy of Prompt Modifiers for Text-To-Image — https://arxiv.org/abs/2204.13988
• DALL·E: Creating Images from Text — https://arxiv.org/abs/2102.12092
• Design Guidelines for Prompt Engineering Text-to-Image — https://arxiv.org/abs/2109.06977
• High-Resolution Image Synthesis with Latent Diffusion Models — https://arxiv.org/abs/2112.10752

• AudioLM — https://arxiv.org/pdf/2209.03143
• ERNIE-Music — https://arxiv.org/pdf/2302.04456
• Make-An-Audio — https://arxiv.org/pdf/2301.12661.pdf
• MusicLM — https://arxiv.org/abs/2301.11325
• Noise2Music — https://arxiv.org/abs/2301.11325

• AudioLM — https://arxiv.org/pdf/2209.03143
• Dreamix — https://arxiv.org/pdf/2302.01329.pdf
• Noise2Music — https://arxiv.org/abs/2301.11325
• Tune-A-Video — https://arxiv.org/pdf/2212.11565.pdf

• Piloting Copilot and Codex — https://arxiv.org/abs/2210.14699

• AI Config — https://github.com/lastmile-ai/aiconfig
• Agenta — https://github.com/agenta-ai/agenta
• Arize-Phoenix — https://github.com/Arize-ai/phoenix
• Better Prompt — https://github.com/krrishdholakia/betterprompt
• CometLLM — https://github.com/comet-ml/comet-llm
• Embedchain — https://github.com/embedchain/embedchain
• Haystack — https://github.com/deepset-ai/haystack
• Interactive Composition Explorer (ICE) — https://github.com/oughtinc/ice
• LangChain — https://github.com/hwchase17/langchain
• LastMile AI — https://lastmileai.dev/
• LlamaIndex — https://github.com/jerryjliu/gpt_index
• OpenPrompt — https://github.com/thunlp/OpenPrompt
• PROMPTMETHEUS — https://promptmetheus.com
• Prompt Engine — https://github.com/microsoft/prompt-engine
• Prompt Source — https://github.com/bigscience-workshop/promptsource
• Prompts AI — https://github.com/sevazhidkov/prompts-ai
• Promptify — https://github.com/promptslab/Promptify
• PromptInject — https://github.com/agencyenterprise/PromptInject
• Promptotype — https://www.promptotype.io
• ThoughtSource — https://github.com/OpenBioLink/ThoughtSource
• XpulsAI — https://xpuls.ai/

• Anthropic — https://www.anthropic.com/
• CohereAI — https://cohere.ai/
• FLAN-T5 XXL (HuggingFace Inference API) — https://huggingface.co/docs/api-inference/index
• OpenAI — https://openai.com/api/

• Awesome ChatGPT Prompts — https://github.com/f/awesome-chatgpt-prompts
• Midjourney Prompts — https://huggingface.co/datasets/succinctly/midjourney-prompts
• P3 (Public Pool of Prompts) — https://huggingface.co/datasets/bigscience/P3
• Writing Prompts — https://www.kaggle.com/datasets/ratthachat/writing-prompts

• Bloom — https://huggingface.co/bigscience/bloom
• ChatGPT — https://chat.openai.com/
• Codex — https://platform.openai.com/docs/models/codex
• Facebook LLM (OPT-175B) — https://opt.alpa.ai/
• FLAN-T5 XXL — https://huggingface.co/google/flan-t5-xxl
• GLM-130B — https://github.com/THUDM/GLM-130B
• GPT-J — https://huggingface.co/docs/transformers/model_doc/gptj
• GPT-Neo — https://github.com/EleutherAI/gpt-neo
• GPT-NeoX-20B — https://huggingface.co/docs/transformers/model_doc/gpt_neox
• LaMDA-rlhf-pytorch — https://github.com/conceptofmind/LaMDA-rlhf-pytorch
• Mixtral-84B — https://huggingface.co/docs/transformers/model_doc/mixtral
• PaLM-rlhf-pytorch — https://github.com/lucidrains/PaLM-rlhf-pytorch
• RLHF (instructGOOSE) — https://github.com/xrsrke/instructGOOSE
• XLM-RoBERTa-XL — https://huggingface.co/facebook/xlm-roberta-xxl

• AI Text Classifier — https://platform.openai.com/ai-text-classifier
• GPT-2 Output Detector — https://huggingface.co/spaces/openai/openai-detector
• OpenAI Detector (Python wrapper) — https://github.com/promptslab/openai-detector

• ChatGPT Prompt Engineering for Developers — https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
• Prompt Engineering for Vision Models — https://www.deeplearning.ai/short-courses/prompt-engineering-for-vision-models/

• 3 Principles for prompt engineering with GPT-3 — https://www.linkedin.com/pulse/3-principles-prompt-engineering-gpt-3-ben-whately
• A Complete Introduction to Prompt Engineering — https://www.mihaileric.com/posts/a-complete-introduction-to-prompt-engineering
• Best practices for prompt engineering with OpenAI API — https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api
• DALLE Prompt Book — https://dallery.gallery/the-dalle-2-prompt-book
• Generative AI with Cohere: Part 1 — https://txt.cohere.ai/generative-ai-part-1
• Methods of prompt programming — https://generative.ink/posts/methods-of-prompt-programming
• Microsoft Prompt Engineering Guide — https://microsoft.github.io/prompt-engineering
• OpenAI Cookbook — https://github.com/openai/openai-cookbook
• Prompt Engineering 101 — https://humanloop.com/blog/prompt-engineering-101
• Prompt Engineering 101 — Introduction & resources — https://www.linkedin.com/pulse/prompt-engineering-101-introduction-resources-amatriain
• Prompt Engineering Guide by SudalaiRajkumar — https://github.com/SudalaiRajkumar/Talks_Webinars/blob/master/Slides/PromptEngineering_20230208.pdf
• Stable Diffusion – Best 100+ Prompts — https://mpost.io/best-100-stable-diffusion-prompts-the-most-beautiful-ai-text-to-image-prompts

• Advanced ChatGPT Prompt Engineering — https://www.youtube.com/watch?v=bBiTR_1sEmI
• ChatGPT Guide: 10x Your Results with Better Prompts — https://www.youtube.com/watch?v=os-JX1ZQwIA
• CMU Advanced NLP 2022: Prompting — https://youtube.com/watch?v=5ef83Wljm-M&feature=shares
• Language Models and Prompt Engineering: Systematic Survey — https://youtube.com/watch?v=OsbUfL8w-mo&feature=shares
• Prompt Engineering — A new profession? — https://www.youtube.com/watch?v=w102J3_9Bcs&ab_channel=PatrickDebois
• Prompt Engineering 101: Autocomplete, Zero/One/Few-shot — https://youtube.com/watch?v=v2gD8BHOaX4&feature=shares

• Learn Prompting — https://discord.gg/7enStJXQzD
• MidJourney Discord — https://discord.com/invite/MidJourney
• OpenAI Discord — https://discord.com/invite/openai
• PromptsLab Discord — https://discord.gg/m88xfYMbK6
• r/ChatGPT Discord — https://discord.com/invite/r-chatgpt-1050422060352024636

This repository is stewarded by Fourth Industrial Systems Corporation (4th.is). Contributions are welcome—please review contributing.md before submitting.

Released under Apache 2.0. Original image credit: docs.cohere.ai

This repository is a fork of ai-boost/awesome-prompts.
It has been extended, reorganized, and rebranded by Fourth Industrial Systems Corporation (4th.is) to provide an enterprise-ready, curated catalog with additional resources, structure, and guidance for Prompt Engineering.