Let’s skip the part where I explain what prompt engineering is. You’re here because you’ve already burned hours getting garbage outputs from GPT, watched your token costs spiral, and wondered why the same prompt works beautifully on Monday and completely falls apart on Tuesday.
If you want to read the full guide with all the working examples and detailed explanations, I suggest checking out the original article here: 10 GitHub Repos Every Serious Prompt Write
The difference between developers who ship reliable AI features and those who don’t? They’ve stopped guessing and started engineering their prompts systematically.
These 10 repositories are where the serious work happens. Just the resources that actually move the needle for everyone.
The Knowledge Base
1. dair-ai/Prompt-Engineering-Guide
66k+ stars | The one everyone references for a reason
This is the Wikipedia of prompt engineering, except it’s actually good. DAIR.AI built a complete knowledge system, covering everything from zero-shot basics to bleeding-edge techniques like tree-of-thought and self-consistency.
Three million people have learned from this guide. It’s been cited by The Wall Street Journal and Forbes. Available in 13 languages.
dair-ai
/
Prompt-Engineering-Guide
🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
Prompt Engineering Guide
Sponsored by 
Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs). Researchers use prompt engineering to improve the capacity of LLMs on a wide range of common and complex tasks such as question answering and arithmetic reasoning. Developers use prompt engineering to design robust and effective prompting techniques that interface with LLMs and other tools.
Motivated by the high interest in developing with LLMs, we have created this new prompt engineering guide that contains all the latest papers, learning guides, lectures, references, and tools related to prompt engineering for LLMs.
🌐 Prompt Engineering Guide (Web Version)
🎉 We are excited to launch our new prompt engineering, RAG, and AI Agents courses under the…
When you’re debugging why chain-of-thought isn’t working or need to explain RAG to your team, this is where you’ll end up anyway. Bookmark it now.
You only need quick templates, not deep understanding.
2. NirDiamant/Prompt_Engineering
22 runnable notebooks | Learn by breaking things
Reading about few-shot prompting is one thing. Running a Jupyter notebook that lets you swap examples and watch outputs change in real-time? That’s how the concepts actually stick.
This repo gives you 22 hands-on tutorials covering the full spectrum — basic prompting, chain-of-thought, self-consistency, prompt chaining, and more. Each technique comes with executable code you can fork and destroy.
You’ll learn more in an afternoon of experimenting with these notebooks than a week of reading blog posts.
NirDiamant
/
Prompt_Engineering
This repository offers a comprehensive collection of tutorials and implementations for Prompt Engineering techniques, ranging from fundamental concepts to advanced strategies. It serves as an essential resource for mastering the art of effectively communicating with and leveraging large language models in AI applications.
🌟 Support This Project: Your sponsorship fuels innovation in prompt engineering development. Become a sponsor to help maintain and expand this valuable resource!
Prompt Engineering Techniques: Comprehensive Repository for Development and Implementation 🖋️
Welcome to one of the most extensive and dynamic collections of Prompt Engineering tutorials and implementations available today. This repository serves as a comprehensive resource for learning, building, and sharing prompt engineering techniques, ranging from basic concepts to advanced strategies for leveraging large language models.
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Introduction
Prompt engineering is at the forefront of artificial intelligence, revolutionizing the way we interact with…
You hate Jupyter notebooks with a passion.
3. microsoft/generative-ai-for-beginners
21 lessons | The structured path
Microsoft’s Cloud Advocates built a full curriculum for generative AI — and the prompt engineering sections are legitimately useful, not just marketing filler.
The course walks you from fundamentals through building actual applications. You’ll understand prompting in context, not as an isolated skill.
If your learning style is “give me a syllabus and let me work through it,” this is your repo.
microsoft
/
generative-ai-for-beginners
21 Lessons, Get Started Building with Generative AI
21 Lessons teaching everything you need to know to start building Generative AI applications
🌐 Multi-Language Support
Supported via GitHub Action (Automated & Always Up-to-Date)
Arabic | Bengali | Bulgarian | Burmese (Myanmar) | Chinese (Simplified) | Chinese (Traditional, Hong Kong) | Chinese (Traditional, Macau) | Chinese (Traditional, Taiwan) | Croatian | Czech | Danish | Dutch | Estonian | Finnish | French | German | Greek | Hebrew | Hindi | Hungarian | Indonesian | Italian | Japanese | Korean | Lithuanian | Malay | Marathi | Nepali | Norwegian | Persian (Farsi) | Polish | Portuguese (Brazil) | Portuguese (Portugal) | Punjabi (Gurmukhi) | Romanian | Russian | Serbian (Cyrillic) | Slovak | Slovenian | Spanish | Swahili | Swedish | Tagalog (Filipino) | Tamil | Thai | Turkish | Ukrainian | Urdu | Vietnamese
Generative AI for Beginners (Version 3) - A Course
Learn the fundamentals of…
You already ship AI features in production and need advanced optimization, not fundamentals.
The Prompt Vaults
4. f/awesome-chatgpt-prompts
134k+ stars | The prompt library that started it all
134,000 stars don’t lie. This is the largest community-curated prompt collection on GitHub, and it works across ChatGPT, Claude, Gemini, Llama, and basically anything that accepts text.
Need a prompt that turns GPT into a Linux terminal? A Socratic tutor? A regex generator? Someone’s already written and refined it.
Why reinvent the wheel? These prompts are battle-tested by millions of users. Use them as starting points, then customize.
f
/
awesome-chatgpt-prompts
This repo includes ChatGPT prompt curation to use ChatGPT and other LLM tools better.
Sponsors
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If you're building MCPs, MCP Tools is a Swiss-army knife for MCP Servers.
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Welcome to the "Awesome ChatGPT Prompts" repository! While this collection was originally created for ChatGPT, these prompts work great with other AI models like Claude, Gemini, Hugging Face Chat, Llama, Mistral, and more.
ChatGPT is a web interface created by OpenAI that provides access to their GPT (Generative Pre-trained Transformer) language models. The underlying models, like GPT-4o and GPT-o1, are large language models trained on vast amounts of text data that can understand and generate human-like text. Like other AI chat interfaces, you can provide prompts and have natural conversations with the AI, which will generate contextual responses based on the conversation history and your inputs.
In this…
The catch **is **Quality varies. Some prompts are brilliant; others are gimmicks. You’ll need to curate.
5. ai-boost/awesome-prompts
7k+ stars | Prompts from GPTs that actually work
Here’s something the other lists don’t give you: prompts extracted from top-rated GPTs in the GPTs Store. These aren’t hypothetical templates, they’re prompts powering applications people actually use and pay for.
The repo also aggregates research papers on chain-of-thought, ReAct, self-consistency, and other advanced techniques. Theory meets practice.
Studying prompts from successful GPT applications teaches you what works in production, not just what sounds good in a tutorial.
ai-boost
/
awesome-prompts
Curated list of chatgpt prompts from the top-rated GPTs in the GPTs Store. Prompt Engineering, prompt attack & prompt protect. Advanced Prompt Engineering papers.
Awesome-GPTs-Prompts🪶
English | Deutsch | Español | français | 日本語 | 한국어 | Português | Русский | 中文
This repository contains a curated list of awesome prompts on OpenAI GPT store.
🚀 Welcome to Awesome-GPTs-Prompts! 🌟
👋 Discover the secret prompts of top GPTs (from the official GPT Store )! Share and explore the most enchanting prompts from renowned GPTs. 🤩
🔥 Features:
- Top GPT Prompts: Unveil the magic behind the best GPTs! 🥇
- Community Sharing: Join the github repo for exchanging brilliant GPT prompts! 💬
- Prompt Showcase: Got an amazing prompt? Share it and inspire others! ✨
🌈 Join us in shaping the future of AI with every prompt you share! 🌐
Thank you! Your stars🌟 and recommendations are what make this community vibrant!
Table of Contents
You’re looking for beginner-friendly examples. This repo assumes you know the basics.
6. hwchase17/langchain-hub
Production-ready | For LangChain builders
If you’re building with LangChain, stop writing prompts from scratch. The LangChain Hub organizes battle-tested prompts by use case — summarization, QA, agents, SQL generation — with documentation explaining how to plug them into your chains.
These prompts are designed for production patterns, not demos. Each one comes with context on when and how to use it.
LangChainHub
| 🌐 This repo is getting replaced by our hosted LangChain Hub Product! Visit it at https://smith.langchain.com/hub 🌐 |
|---|
Introduction
Taking inspiration from Hugging Face Hub, LangChainHub is collection of all artifacts useful for working with LangChain primitives such as prompts, chains and agents The goal of this repository is to be a central resource for sharing and discovering high quality prompts, chains and agents that combine together to form complex LLM applications.
We are starting off the hub with a collection of prompts, and we look forward to the LangChain community adding to this collection. We hope to expand to chains and agents shortly.
Contributing
Since we are using GitHub to organize this Hub, adding artifacts can best be done in one of three ways:
- Create a fork and then open a PR against the repo.
- Create an issue on the repo with details of the artifact you would like…
You’re not using LangChain. These prompts work best within that ecosystem.
The Research Stacks
7. promptslab/Awesome-Prompt-Engineering
The academic deep-dive
Want to understand why chain-of-thought works, not just how to use it? This repo curates the research papers that define the field, AutoPrompt, few-shot learning, prompt tuning, reasoning techniques.
It’s dense. It’s academic. And if you’re building anything serious, understanding the foundations will save you from cargo-culting techniques that don’t apply to your use case.
promptslab
/
Awesome-Prompt-Engineering
This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc
Awesome Prompt Engineering 🧙♂️
This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc
Prompt Engineering Course is coming soon
Prompt Engineering Course is coming soon
Table of Contents
- Papers
- Tools & Code
- Apis
- Datasets
- Models
- AI Content Detectors
- Educational
- Videos
- Books
- Communities
- How to Contribute
Papers
📄
-
Prompt Engineering Techniques:
- Text Mining for Prompt Engineering: Text-Augmented Open Knowledge Graph Completion via PLMs [2023] (ACL)
- A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT [2023] (Arxiv)
- Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery [2023] (Arxiv)
- Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models [2023] (Arxiv)
- Progressive Prompts: Continual Learning for Language Models [2023] (Arxiv)
- Batch Prompting: Efficient Inference with LLM APIs [2023] (Arxiv)
- Successive Prompting for Decompleting Complex Questions [2022] (Arxiv)
- Structured Prompting: Scaling In-Context Learning to 1,000 Examples [2022] (Arxiv)
- Large Language Models Are Human-Level…
The developers who read the papers build better systems than those who just copy prompts from Twitter.
You need quick wins, not deep theory.
8. snwfdhmp/awesome-gpt-prompt-engineering
The ecosystem map
Less about prompts themselves, more about everything around them. This repo maps the landscape: prompt communities like FlowGPT, marketplaces like PromptBase, AI tool directories, job boards for prompt engineers, courses, and newsletters.
If you’re going deep on prompt engineering, maybe even building a career around it, this is your directory to the ecosystem.
snwfdhmp
/
awesome-gpt-prompt-engineering
A curated list of awesome resources, tools, and other shiny things for LLM prompt engineering.
Awesome GPT Prompt Engineering 
A curated list of awesome resources, tools, and other shiny things for GPT prompt engineering.
Consider giving it a ⭐️ if you like it to show your support!
Table of Contents
Roadmaps
- Prompt Engineering Roadmap: Step by step guide to learning Prompt Engineering.
Guides
- Learn Prompt Engineering: Introduction to Prompt Engineering and Prompt Engineering techniques.
- Prompt Engineering Guide: Guides, papers, lecture, notebooks and resources for prompt engineering.
- Prompt Engineering 101: Prompt Engineering guide by Xavi.
- Prompt Engineering 101: Prompt Engineering guide by Raza Habib & Sinan Ozdemir.
- Prompt Engineering Guide: Prompt Engineering guide by Sudalai Rajkumar.
- How to generate text: using different decoding methods for language generation with…
You just want to write better prompts, not explore the industry.
The Power Tools
9. mshumer/gpt-prompt-engineer
Automated optimization | Stop guessing
Here’s the problem with manual prompt iteration, it’s slow, inconsistent, and biased by whatever test cases you happen to remember.
This tool generates prompt variations automatically, tests them against your criteria, and ranks results using an ELO rating system. Prompts compete head-to-head. The best ones rise to the top. No gut feelings required.
For any prompt that runs thousands of times in production, systematic optimization pays for itself immediately. A 5% improvement in accuracy compounds fast.
gpt-prompt-engineer
Be the first to know when I publish new AI builds + demos!
Overview
Prompt engineering is kind of like alchemy. There's no clear way to predict what will work best. It's all about experimenting until you find the right prompt. gpt-prompt-engineer is a tool that takes this experimentation to a whole new level.
Simply input a description of your task and some test cases, and the system will generate, test, and rank a multitude of prompts to find the ones that perform the best.
New 3/20/24: The Claude 3 Opus Version
I've added a new version of gpt-prompt-engineer that takes full advantage of Anthropic's Claude 3 Opus model. This version auto-generates test cases and allows for the user to define multiple input variables, making it even more powerful and flexible. Try it out with the claude-prompt-engineer.ipynb notebook in the repo!
New 3/20/24: Claude 3 Opus -> Haiku Conversion
…You’re prototyping, not optimizing. This is overkill for one-off prompts.
10. langfuse/langfuse
Open-source prompt ops | The production layer
At some point, “I’ll just update the prompt in the code” stops working. You need version control, you need to A/B test changes, or you need to know which prompt version caused that regression last Tuesday.
Langfuse is open-source LLM infrastructure that handles prompt management, observability, and evaluation. Version control your prompts. Test them in a playground before shipping. Track performance across deployments.
Built by a YC W23 team. Self-host or use their cloud. Integrates with OpenAI, LangChain, LiteLLM, and more.
If you’re running prompts in production, you need this layer. The alternative is spreadsheets and prayer.
langfuse
/
langfuse
🪢 Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23
Langfuse uses GitHub Discussions for Support and Feature Requests.
We're hiring. Join us in product engineering and technical go-to-market roles.
Langfuse is an open source LLM engineering platform. It helps teams collaboratively develop, monitor, evaluate, and debug AI applications. Langfuse can be self-hosted in minutes and is battle-tested.
✨ Core Features
-
LLM Application Observability: Instrument your app and start ingesting traces to Langfuse, thereby tracking LLM calls and other relevant logic in your app such as retrieval, embedding, or agent actions. Inspect and debug complex logs and user sessions. Try the interactive demo to see this in action.
-
Prompt Management helps you centrally manage, version control, and collaboratively iterate on your prompts. Thanks to strong caching on server and client side, you…
You’re still experimenting. This is infrastructure for teams shipping real products.
The Decision Tree
Prompt-Engineering-Guide for foundations:
- Awesome-chatgpt-prompts for immediate templates
- Generative-ai-for-beginners if you want structure
Building production features?
- langchain-hub for battle-tested templates
- gpt-prompt-engineer to optimize critical prompts
- langfuse to manage everything
Going deep on the craft?
- NirDiamant’s notebooks for hands-on practice
- promptslab’s research collection for academic foundations
- ai-boost’s prompts to study what works in real GPTs
The Meta-Point
Here’s what separates good developers from great ones where AI is booming, the great ones treat prompting as engineering, not guessing.
They version control their prompts. They test systematically, understand why techniques work, not just how to copy-paste them, and build on the collective knowledge of communities instead of reinventing everything from scratch.
These 10 repositories represent thousands of hours of work from developers who’ve already solved the problems you’re facing, use them.
Your prompts, and your token bills, will thank you.
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