AI Engineering Hub
A collection of hands-on AI tutorials and sample projects covering LLMs, RAG, and agents.
- Stars
- 40.8k
- Forks
- 6.5k
- Licence
- MIT
- Language
- Jupyter Notebook
- Last update
- 5 days ago
- Type
- Learning resource
What it is
AI Engineering Hub is a large collection of tutorial projects, mostly Jupyter notebooks and small apps, that show how to build with large language models, retrieval-augmented generation (RAG), and AI agents. The README groups the projects into beginner, intermediate, and advanced levels, and links to a newsletter and an AI Engineering Roadmap for newcomers. It is a set of examples to study and adapt rather than a single program to install.
- Follow a beginner tutorial that builds a simple RAG pipeline with LlamaIndex and Ollama
- Study an MCP-based agentic RAG project to see how agents call tools
- Compare how different models handle code generation using the included comparison projects
- Run a local OCR or chat interface using Llama or Gemma models, as described in the project list
Great for
- Developers who want working examples of RAG or agent systems to adapt
- Beginners who want to follow guided projects, starting with the beginner list
- Students learning how LLM tools fit together in practice
- Engineers comparing models or agent frameworks through the included examples
Not ideal for
- People looking for one ready-made application to install and use, since the repo is a collection of separate projects
- Readers who need guaranteed up-to-date code, because the README does not state how each project is maintained
- Non-programmers, since most projects assume Python and familiarity with notebooks
How to get it
- 1
Clone and build from source
You'll need: Git · Python (version not stated in the README excerpt)
Clone the repository
git clone https://github.com/patchy631/ai-engineering-hub - 2
Run it
- Clone the repository and open the folder for a beginner project, such as Simple RAG Workflow
- Read that project's own README for its dependencies and setup steps
- Install the listed requirements and any API keys the project needs
- Run the notebook or app and work through the example
- 3
Get the most out of it
- Start with the beginner projects and the AI Engineering Roadmap linked from the README
- Read the difficulty grouping to move from basic RAG to agent and MCP projects
- Use the evaluation and observability projects to learn how to measure RAG quality
- Subscribe to the linked newsletter if you want the free eBook mentioned in the README
Downloads
Strengths
- Covers a wide range of topics, from OCR and chat UIs to MCP, voice agents, and fine-tuning
- Projects are sorted by difficulty, which helps beginners choose a starting point
- Released under the MIT license, so the material can be reused with few restrictions
Trade-offs
- The README does not say how often individual projects are updated, and each project has its own setup
- Many projects depend on paid or third-party services such as AssemblyAI, Cartesia, or Firecrawl, whose requirements the README only mentions by name
- The README is truncated here, so the full list of projects and their setup details is not covered
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| ai-engineering-hub (this) | 40.8k | Learning resource | intermediate | 5 days ago | Developers who want working examples of RAG or agent systems to adapt |
| langchain | 149k | Framework | intermediate | 4 days ago | Python developers building LLM-powered applications |
| llama_index | 52.8k | Library | intermediate | 10 days ago | Python developers building retrieval-augmented generation (RAG) apps over private documents |
| crewAI | 60.4k | Library | intermediate | 4 days ago | Python developers prototyping multi-agent workflows |
| Prompt-Engineering-Guide | 79.8k | Learning resource | beginner | 8 months ago | People who are new to prompting and want structured lessons with examples |
| ai-engineering-from-scratch | 76.3k | Learning resource | intermediate | 4 days ago | Students who want to understand how LLMs, retrieval, and agents work internally |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.