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).
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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.