base on This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc <h2 align="center">Awesome Prompt Engineering 🧙‍♂️ </h2> <p align="center"> <img width="650" src="https://raw.githubusercontent.com/promptslab/Awesome-Prompt-Engineering/main/_source/prompt.png"> </p> <p align="center"> <p align="center"> This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc </p> <h4 align="center"> ``` Prompt Engineering Course is coming soon.. ``` <a href="https://awesome.re"> <img src="https://awesome.re/badge.svg" alt="Awesome" /> </a> <a href="https://github.com/promptslab/Awesome-Prompt-Engineering/blob/main/LICENSE"> <img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" alt="Awesome-Prompt-Engineering is released under the Apache 2.0 license." /> </a> <a href="http://makeapullrequest.com"> <img src="https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square" alt="http://makeapullrequest.com" /> </a> <a href="https://discord.gg/m88xfYMbK6"> <img src="https://img.shields.io/badge/Discord-Community-orange" alt="Community" /> </a> <a href="https://colab.research.google.com/drive/1f4YG9stX9aHmsmh6ZhzjekJU4X4BIynO?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="colab" /> </a> </h4> # Table of Contents - [Papers](#papers) - [Tools & Code](#tools--code) - [Apis](#apis) - [Datasets](#datasets) - [Models](#models) - [AI Content Detectors](#ai-content-detectors) - [Educational](#educational) - [Courses](#courses) - [Tutorials](#tutorials) - [Videos](#videos) - [Books](#books) - [Communities](#communities) - [How to Contribute](#how-to-contribute) ## Papers 📄 - **Prompt Engineering Techniques**: - [Text Mining for Prompt Engineering: Text-Augmented Open Knowledge Graph Completion via PLMs](https://aclanthology.org/2023.findings-acl.709.pdf) [2023] (ACL) - [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://arxiv.org/abs/2302.11382) [2023] (Arxiv) - [Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery](https://arxiv.org/abs/2302.03668) [2023] (Arxiv) - [Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models](https://arxiv.org/abs/2302.00618) [2023] (Arxiv) - [Progressive Prompts: Continual Learning for Language Models](https://arxiv.org/abs/2301.12314) [2023] (Arxiv) - [Batch Prompting: Efficient Inference with LLM APIs](https://arxiv.org/abs/2301.08721) [2023] (Arxiv) - [Successive Prompting for Decompleting Complex Questions](https://arxiv.org/abs/2212.04092) [2022] (Arxiv) - [Structured Prompting: Scaling In-Context Learning to 1,000 Examples](https://arxiv.org/abs/2212.06713) [2022] (Arxiv) - [Large Language Models Are Human-Level Prompt Engineers](https://arxiv.org/abs/2211.01910) [2022] (Arxiv) - [Ask Me Anything: A simple strategy for prompting language models](https://paperswithcode.com/paper/ask-me-anything-a-simple-strategy-for) [2022] (Arxiv) - [Prompting GPT-3 To Be Reliable](https://arxiv.org/abs/2210.09150) [2022](Arxiv) - [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://arxiv.org/abs/2210.02406) [2022] (Arxiv) - [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://arxiv.org/abs/2203.06566) [2022] (Arxiv) - [Investigating Prompt Engineering in Diffusion Models](https://arxiv.org/abs/2211.15462) [2022] (Arxiv) - [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://arxiv.org/abs/2112.00114) [2021] (Arxiv) - [Reframing Instructional Prompts to GPTk's Language](https://arxiv.org/abs/2109.07830) [2021] (Arxiv) - [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://arxiv.org/abs/2104.08786) [2021] (Arxiv) - [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/abs/2104.08691) [2021] (Arxiv) - [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://arxiv.org/abs/2102.07350) [2021] (Arxiv) - [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://arxiv.org/abs/2101.00190) [2021] (Arxiv) - **Reasoning and In-Context Learning**: - [Multimodal Chain-of-Thought Reasoning in Language Models](https://arxiv.org/abs/2302.00923) [2023] (Arxiv) - [On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://arxiv.org/abs/2212.08061) [2022] (Arxiv) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) [2022] (Arxiv) - [Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought](https://arxiv.org/abs/2210.01240v3) [2022] (Arxiv) - [On the Advance of Making Language Models Better Reasoners](https://arxiv.org/abs/2206.02336) [2022] (Arxiv) - [Large Language Models are Zero-Shot Reasoners](https://arxiv.org/abs/2205.11916) [2022] (Arxiv) - [Reasoning Like Program Executors](https://arxiv.org/abs/2201.11473) [2022] (Arxiv) - [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://arxiv.org/abs/2203.11171) [2022] (Arxiv) - [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://arxiv.org/abs/2202.12837) [2022] (Arxiv) - [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://arxiv.org/abs/2209.09513v2) [2022] (Arxiv) - [Chain of Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903) [2021] (Arxiv) - [Generated Knowledge Prompting for Commonsense Reasoning](https://arxiv.org/abs/2110.08387) [2021] (Arxiv) - [BERTese: Learning to Speak to BERT](https://aclanthology.org/2021.eacl-main.316) [2021] (Acl) - **Evaluating and Improving Language Models**: - [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://arxiv.org/abs/2302.00093) [2023] (Arxiv) - [Crawling the Internal Knowledge-Base of Language Models](https://arxiv.org/abs/2301.12810) [2023] (Arxiv) - [Discovering Language Model Behaviors with Model-Written Evaluations](https://arxiv.org/abs/2212.09251) [2022] (Arxiv) - [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://arxiv.org/abs/2102.09690) [2021] (Arxiv) - **Applications of Language Models**: - [Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves](https://arxiv.org/abs/2311.04205) [2023] (Arxiv) - [Prompting for Multimodal Hateful Meme Classification](https://arxiv.org/abs/2302.04156) [2023] (Arxiv) - [PLACES: Prompting Language Models for Social Conversation Synthesis](https://arxiv.org/abs/2302.03269) [2023] (Arxiv) - [Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation](https://arxiv.org/abs/2302.01441) [2023] (Arxiv) - [PAL: Program-aided Language Models](https://arxiv.org/abs/2211.10435) [2023](Arxiv) - [Legal Prompt Engineering for Multilingual Legal Judgement Prediction](https://arxiv.org/abs/2212.02199) [2023] (Arxiv) - [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://arxiv.org/abs/2210.15157) [2022] (Arxiv) - [Plot Writing From Scratch Pre-Trained Language Models](https://aclanthology.org/2022.inlg-main.5) [2022] (Acl) - [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://arxiv.org/abs/2010.15980) [2020] (Arxiv) - **Threat Detection and Adversarial Examples**: - [Constitutional AI: Harmlessness from AI Feedback](https://arxiv.org/abs/2212.08073) [2022] (Arxiv) - [Ignore Previous Prompt: Attack Techniques For Language Models](https://arxiv.org/abs/2211.09527) [2022] (Arxiv) - [Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods](https://arxiv.org/abs/2210.07321) [2022] (Arxiv) - [Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples](https://arxiv.org/abs/2209.02128) [2022] (Arxiv) - [Toxicity Detection with Generative Prompt-based Inference](https://arxiv.org/abs/2205.12390) [2022] (Arxiv) - [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) [2020] (Mit) - **Few-shot Learning and Performance Optimization**: - [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://arxiv.org/abs/2209.11755) [2022] (Arxiv) - [The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning](https://arxiv.org/abs/2205.03401) [2022] (Arxiv) - [Making Pre-trained Language Models Better Few-shot Learners](https://aclanthology.org/2021.acl-long.295) [2021] (Acl) - [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) [2020] (Arxiv) - **Text to Image Generation**: - [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988) [2022] (Arxiv) - [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://arxiv.org/abs/2109.06977) [2021] (Arxiv) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) [2021] (Arxiv) - [DALL·E: Creating Images from Text](https://arxiv.org/abs/2102.12092) [2021] (Arxiv) - **Text to Music/Sound Generation**: - [MusicLM: Generating Music From Text](https://arxiv.org/abs/2301.11325) [2023] (Arxiv) - [ERNIE-Music: Text-to-Waveform Music Generation with Diffusion Models](https://arxiv.org/pdf/2302.04456) [2023] (Arxiv) - [Noise2Music: Text-conditioned Music Generation with Diffusion Models](https://arxiv.org/abs/2301.11325) [2023) (Arxiv) - [AudioLM: a Language Modeling Approach to Audio Generation](https://arxiv.org/pdf/2209.03143) [2023] (Arxiv) - [Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models](https://arxiv.org/pdf/2301.12661.pdf) [2023] (Arxiv) - **Text to Video Generation**: - [Dreamix: Video Diffusion Models are General Video Editors](https://arxiv.org/pdf/2302.01329.pdf) [2023] (Arxiv) - [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://arxiv.org/pdf/2212.11565.pdf) [2022] (Arxiv) - [Noise2Music: Text-conditioned Music Generation with Diffusion Models](https://arxiv.org/abs/2301.11325) [2023) (Arxiv) - [AudioLM: a Language Modeling Approach to Audio Generation](https://arxiv.org/pdf/2209.03143) [2023] (Arxiv) - **Overviews**: - [Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic?](https://arxiv.org/abs/2210.14699) [2022] (Arxiv) ## Tools & Code 🔧 | Name | Description | Url | | :-------------------- | :----------: | :----------: | | **LlamaIndex** | LlamaIndex is a project consisting of a set of data structures designed to make it easier to use large external knowledge bases with LLMs. | [[Github]](https://github.com/jerryjliu/gpt_index) | | **Promptify** | Solve NLP Problems with LLM's & Easily generate different NLP Task prompts for popular generative models like GPT, PaLM, and more with Promptify | [[Github]](https://github.com/promptslab/Promptify) | | **Arize-Phoenix** | Open-source tool for ML observability that runs in your notebook environment. Monitor and fine tune LLM, CV and Tabular Models. | [[Github]](https://github.com/Arize-ai/phoenix) | | **Better Prompt** | Test suite for LLM prompts before pushing them to PROD | [[Github]](https://github.com/krrishdholakia/betterprompt) | | **CometLLM** | Log, visualize, and evaluate your LLM prompts, prompt templates, prompt variables, metadata, and more. | [[Github]](https://github.com/comet-ml/comet-llm) | | **Embedchain** | Framework to create ChatGPT like bots over your dataset | [[Github]](https://github.com/embedchain/embedchain) | | **Interactive Composition Explorerx** | ICE is a Python library and trace visualizer for language model programs. | [[Github]](https://github.com/oughtinc/ice) | | **Haystack** | Open source NLP framework to interact with your data using LLMs and Transformers. | [[Github]](https://github.com/deepset-ai/haystack) | | **LangChainx** | Building applications with LLMs through composability | [[Github]](https://github.com/hwchase17/langchain) | | **OpenPrompt** | An Open-Source Framework for Prompt-learning | [[Github]](https://github.com/thunlp/OpenPrompt) | | **Prompt Engine** | This repo contains an NPM utility library for creating and maintaining prompts for Large Language Models (LLMs). | [[Github]](https://github.com/microsoft/prompt-engine) | | **PromptInject** | PromptInject is a framework that assembles prompts in a modular fashion to provide a quantitative analysis of the robustness of LLMs to adversarial prompt attacks. | [[Github]](https://github.com/agencyenterprise/PromptInject) | | **Prompts AI** | Advanced playground for GPT-3 | [[Github]](https://github.com/sevazhidkov/prompts-ai) | | **Prompt Source** | PromptSource is a toolkit for creating, sharing and using natural language prompts. | [[Github]](https://github.com/bigscience-workshop/promptsource) | | **ThoughtSource** | A framework for the science of machine thinking | [[Github]](https://github.com/OpenBioLink/ThoughtSource) | | **PROMPTMETHEUS** | One-shot Prompt Engineering Toolkit | [[Tool]](https://promptmetheus.com) | | **AI Config** | An Open-Source configuration based framework for building applications with LLMs | [[Github]](https://github.com/lastmile-ai/aiconfig) | | **LastMile AI** | Notebook-like playground for interacting with LLMs across different modalities (text, speech, audio, image) | [[Tool]](https://lastmileai.dev/) | | **XpulsAI** | Effortlessly build scalable AI Apps. AutoOps platform for AI & ML | [[Tool]](https://xpuls.ai/) | | **Agenta** | Agenta is an open-source LLM developer platform with the tools for prompt management, evaluation, human feedback, and deployment all in one place. | [[Github]](https://github.com/agenta-ai/agenta) | | **Promptotype** | Develop, test, and monitor your LLM { structured } tasks | [[Tool]](https://www.promptotype.io) | ## Apis 💻 | Name | Description | Url | Paid or Open-Source | | :-------------------- | :----------: | :----------: | :----------: | | **OpenAI** | GPT-n for natural language tasks, Codex for translates natural language to code, and DALL·E for creates and edits original images | [[OpenAI]](https://openai.com/api/) | Paid | | **CohereAI** | Cohere provides access to advanced Large Language Models and NLP tools through one API | [[CohereAI]](https://cohere.ai/) | Paid | | **Anthropic** | Coming soon | [[Anthropic]](https://www.anthropic.com/) | Paid | | **FLAN-T5 XXL** | Coming soon | [[HuggingFace]](https://huggingface.co/docs/api-inference/index) | Open-Source | ## Datasets 💾 | Name | Description | Url | | :-------------------- | :----------: | :----------: | | **P3 (Public Pool of Prompts)** | P3 (Public Pool of Prompts) is a collection of prompted English datasets covering a diverse set of NLP tasks. | [[HuggingFace]](https://huggingface.co/datasets/bigscience/P3) | | **Awesome ChatGPT Prompts** | Repo includes ChatGPT prompt curation to use ChatGPT better. | [[Github]](https://github.com/f/awesome-chatgpt-prompts) | | **Writing Prompts** | Collection of a large dataset of 300K human-written stories paired with writing prompts from an online forum(reddit) | [[Kaggle]](https://www.kaggle.com/datasets/ratthachat/writing-prompts) | | **Midjourney Prompts** | Text prompts and image URLs scraped from MidJourney's public Discord server | [[HuggingFace]](https://huggingface.co/datasets/succinctly/midjourney-prompts) | ## Models 🧠 | Name | Description | Url | | :-------------------- | :----------: | :----------: | | **ChatGPT** | ChatGPT | [[OpenAI]](https://chat.openai.com/) | | **Codex** | The Codex models are descendants of our GPT-3 models that can understand and generate code. Their training data contains both natural language and billions of lines of public code from GitHub | [[Github]](https://platform.openai.com/docs/models/codex) | | **Bloom** | BigScience Large Open-science Open-access Multilingual Language Model | [[HuggingFace]](https://huggingface.co/bigscience/bloom) | | **Facebook LLM** | OPT-175B is a GPT-3 equivalent model trained by Meta. It is by far the largest pretrained language model available with 175 billion parameters. | [[Alpa]](https://opt.alpa.ai/) | | **GPT-NeoX** | GPT-NeoX-20B, a 20 billion parameter autoregressive language model trained on the Pile | [[HuggingFace]](https://huggingface.co/docs/transformers/model_doc/gpt_neox) | | **FLAN-T5 XXL** | Flan-T5 is an instruction-tuned model, meaning that it exhibits zero-shot-like behavior when given instructions as part of the prompt. | [[HuggingFace/Google]](https://huggingface.co/google/flan-t5-xxl) | | **XLM-RoBERTa-XL** | XLM-RoBERTa-XL model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. | [[HuggingFace]](https://huggingface.co/facebook/xlm-roberta-xxl) | | **GPT-J** | It is a GPT-2-like causal language model trained on the Pile dataset | [[HuggingFace]](https://huggingface.co/docs/transformers/model_doc/gptj) | | **PaLM-rlhf-pytorch** | Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM | [[Github]](https://github.com/lucidrains/PaLM-rlhf-pytorch) | **GPT-Neo** | An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library. | [[Github]](https://github.com/EleutherAI/gpt-neo) | | **LaMDA-rlhf-pytorch** | Open-source pre-training implementation of Google's LaMDA in PyTorch. Adding RLHF similar to ChatGPT. | [[Github]](https://github.com/conceptofmind/LaMDA-rlhf-pytorch) | | **RLHF** | Implementation of Reinforcement Learning from Human Feedback (RLHF) | [[Github]](https://github.com/xrsrke/instructGOOSE) | | **GLM-130B** | GLM-130B: An Open Bilingual Pre-Trained Model | [[Github]](https://github.com/THUDM/GLM-130B) | | **Mixtral-84B** | Mixtral-84B is a Mixture of Expert (MOE) model with 8 experts per MLP. | [[HuggingFace]](https://huggingface.co/docs/transformers/model_doc/mixtral) | ## AI Content Detectors 🔎 | Name | Description | Url | | :-------------------- | :----------: | :----------: | | **AI Text Classifier** | The AI Text Classifier is a fine-tuned GPT model that predicts how likely it is that a piece of text was generated by AI from a variety of sources, such as ChatGPT. | [[OpenAI]](https://platform.openai.com/ai-text-classifier) | | **GPT-2 Output Detector** | This is an online demo of the GPT-2 output detector model, based on the 🤗/Transformers implementation of RoBERTa. | [[HuggingFace]](https://huggingface.co/spaces/openai/openai-detector) | | **Openai Detector** | AI classifier for indicating AI-written text (OpenAI Detector Python wrapper) | [[GitHub]](https://github.com/promptslab/openai-detector) | ## Courses 👩‍🏫 - [ChatGPT Prompt Engineering for Developers](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/), by [deeplearning.ai](https://www.deeplearning.ai/) - [Prompt Engineering for Vision Models](https://www.deeplearning.ai/short-courses/prompt-engineering-for-vision-models/) by [DeepLearning.AI](https://www.deeplearning.ai/) ## Tutorials 📚 - **Introduction to Prompt Engineering** - [Prompt Engineering 101 - Introduction and resources](https://www.linkedin.com/pulse/prompt-engineering-101-introduction-resources-amatriain) - [Prompt Engineering 101](https://humanloop.com/blog/prompt-engineering-101) - [Prompt Engineering Guide by SudalaiRajkumar](https://github.com/SudalaiRajkumar/Talks_Webinars/blob/master/Slides/PromptEngineering_20230208.pdf) - **Beginner's Guide to Generative Language Models** - [A beginner-friendly guide to generative language models - LaMBDA guide](https://aitestkitchen.withgoogle.com/how-lamda-works) - [Generative AI with Cohere: Part 1 - Model Prompting](https://txt.cohere.ai/generative-ai-part-1) - **Best Practices for 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) - [How to write good prompts](https://andymatuschak.org/prompts) - **Complete Guide to Prompt Engineering** - [A Complete Introduction to Prompt Engineering for Large Language Models](https://www.mihaileric.com/posts/a-complete-introduction-to-prompt-engineering) - [Prompt Engineering Guide: How to Engineer the Perfect Prompts](https://richardbatt.co.uk/prompt-engineering-guide-how-to-engineer-the-perfect-prompts) - **Technical Aspects of Prompt Engineering** - [3 Principles for prompt engineering with GPT-3](https://www.linkedin.com/pulse/3-principles-prompt-engineering-gpt-3-ben-whately) - [A Generic Framework for ChatGPT Prompt Engineering](https://medium.com/@thorbjoern.heise/a-generic-framework-for-chatgpt-prompt-engineering-7097f6513a0b) - [Methods of prompt programming](https://generative.ink/posts/methods-of-prompt-programming) - **Resources for Prompt Engineering** - [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) - [DALLE Prompt Book](https://dallery.gallery/the-dalle-2-prompt-book) - [OpenAI Cookbook](https://github.com/openai/openai-cookbook) - [Prompt Engineering by Microsoft](https://microsoft.github.io/prompt-engineering) ## Videos 🎥 - [Advanced ChatGPT Prompt Engineering](https://www.youtube.com/watch?v=bBiTR_1sEmI) - [ChatGPT: 5 Prompt Engineering Secrets For Beginners](https://www.youtube.com/watch?v=2zg3V66-Fzs) - [CMU Advanced NLP 2022: Prompting](https://youtube.com/watch?v=5ef83Wljm-M&feature=shares) - [Prompt Engineering - A new profession ?](https://www.youtube.com/watch?v=w102J3_9Bcs&ab_channel=PatrickDebois) - [ChatGPT Guide: 10x Your Results with Better Prompts](https://www.youtube.com/watch?v=os-JX1ZQwIA) - [Language Models and Prompt Engineering: Systematic Survey of Prompting Methods in NLP](https://youtube.com/watch?v=OsbUfL8w-mo&feature=shares) - [Prompt Engineering 101: Autocomplete, Zero-shot, One-shot, and Few-shot prompting](https://youtube.com/watch?v=v2gD8BHOaX4&feature=shares) ## Communities 🤝 - [OpenAI Discord](https://discord.com/invite/openai) - [PromptsLab Discord](https://discord.gg/m88xfYMbK6) - [Learn Prompting](https://discord.gg/7enStJXQzD) - [r/ChatGPT Discord](https://discord.com/invite/r-chatgpt-1050422060352024636) - [MidJourney Discord](https://discord.com/invite/MidJourney) # How to Contribute We welcome contributions to this list! In fact, that's the main reason why I created it - to encourage contributions and encourage people to subscribe to changes in order to stay informed about new and exciting developments in the world of Large Language Models(LLMs) & Prompt-Engineering. Before contributing, please take a moment to review our [contribution guidelines](contributing.md). These guidelines will help ensure that your contributions align with our objectives and meet our standards for quality and relevance. Thank you for your interest in contributing to this project! <h6 align="center"> <small><small>Image Source: docs.cohere.ai </small> </small> </h6> ", Assign "at most 3 tags" to the expected json: {"id":"6625","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"