Prompt engineering is evolving fast, and GitHub is where that evolution lives.
If you’re serious about mastering how AI systems think, these 5 repositories will save you months of trial and error.
1️⃣ OpenAI Cookbook: The Official Playground
github.com/openai/openai-cookbook
The go-to library for developers experimenting with GPT APIs, embeddings, and fine-tuning.
It’s packed with production-ready code from text classification to function calling examples.
Use It For:
- Building your own API workflows
- Understanding token limits & prompt optimisation
- Experimenting with fine-tuning templates
2️⃣ Awesome ChatGPT Prompts: The Community Goldmine
github.com/f/awesome-chatgpt-prompts
This repo is a global collection of the best prompts ever shared.
You’ll find everything from marketing copy to debugging assistants, a perfect place to study structure and tone.
Use It For:
- Reverse-engineering effective prompts
- Building your personal prompt library
- Discovering roles and contexts that get results
3️⃣ LangChain: The Bridge Between Prompts and Apps
github.com/langchain-ai/langchain
LangChain is what turns prompts into workflows.
It helps you connect LLMs with APIs, memory, and databases to build AI agents that actually do things.
Use It For:
- Creating chatbots, research agents, or AI assistants
- Experimenting with chains and tools
- Understanding how context memory works
4️⃣ Prompt-Engineering-Guide by DAIR.AI
github.com/dair-ai/Prompt-Engineering-Guide
An academic-quality guide covering prompt types, design patterns, and evaluations.
It goes beyond examples to explain why certain prompts work, perfect for those who want depth.
Use It For:
- Learning systematic prompt design
- Accessing real papers and benchmarks
- Staying aligned with industry best practices
5️⃣ Jaideep Parashar / AI Prompt Library: Applied AI in Action
github.com/jaideepparashar/AI-Prompt-Library
My own open-source collection of prompt frameworks used in ReThynk AI books and AI Lab projects.
It includes ready-to-use prompts for coding, business automation, Excel integration, and personal branding.
Use It For:
- Real-world AI automation examples
- Developer-ready prompt templates
- Studying how to document and version prompts like code
Final Thought
Don’t just collect prompts, study how they evolve into systems.
GitHub isn’t just a repository of code anymore; it’s a repository of intelligence.
If you bookmark these five today and study their structure, you’ll develop an intuition that no tutorial can teach.
Resources
ChatGPT Prompts for Coding: 630 Actionable Prompts for Debugging, Testing, Integration, and Deployment
Next Article:
“Building a Prompt Engineering Toolkit for Developers”: how to create your own custom toolkit for experiments, testing, and automation.
Top comments (3)
Really solid list — it’s more like a roadmap than a collection of links.
That line about GitHub being a “repository of intelligence” really stands out. I’ve been mixing ideas from DAIR.AI and LangChain lately too — makes a huge difference once you understand why certain prompts work. Thanks for sharing this! 🚀
Thank you so much!
Glad you felt it read like a roadmap; that was exactly the intention. Once we start treating GitHub as a living intelligence library instead of just a code store, everything shifts.
DAIR.AI + LangChain is a powerful combination. The “why it works” layer is what turns prompting from trial-and-error into a real skill.
Don’t just collect prompts, study how they evolve into systems.