10 Best AI Engineering GitHub Repos to Build Real Systems
Source: Dev.to
Introduction
You pick up practical AI engineering faster by digging into real code, real notebooks, and real systems. Breaking things and fixing them often teaches more than watching another lecture. The repositories below are free, well‑structured, and focused on what actually works in practice, helping you move from theory to shipped features. Each pick includes hands‑on materials and clear guidance—lessons, notebooks, examples, and end‑to‑end projects that run without unnecessary setup.
Repository List
1. Hands‑On Large Language Models
Full code from the book, with notebooks covering LLM basics, training, and fine‑tuning. Ideal for a guided, notebook‑first path from foundations to customization.
GitHub – HandsOnLLM/Hands‑On‑Large‑Language‑Models
2. AI Agents for Beginners (Microsoft)
A free, structured 11‑lesson course to start AI agents the right way—think of it as turn‑by‑turn directions for agents, minus the detours.
GitHub – microsoft/ai‑agents‑for‑beginners
3. GenAI Agents (Nir Diamant)
Clear tutorials and implementations of generative AI agent techniques, from basic builds to advanced strategies. Shows how different agent strategies are wired up, making design choices obvious.
GitHub – NirDiamant/GenAI_Agents
4. Basics (Made With ML)
One of the best resources for building production‑grade ML systems end‑to‑end. Perfect when you care about real‑world systems and operational quality, not just pretty notebooks.
GitHub – madewithml/basics
5. Prompt Engineering Guide (DAIR‑AI)
A massive collection of guides, papers, notebooks, and resources on prompt engineering. Handy for proven patterns and quick references in one place.
GitHub – dair‑ai/Prompt‑Engineering‑Guide
6. Hands‑On AI Engineering (Sumanth077)
Curated examples of AI‑powered applications and agentic systems using LLMs that actually run. Shows how the pieces snap together in working examples, saving a lot of guesswork.
GitHub – Sumanth077/Hands‑On‑AI‑Engineering
7. Awesome Generative AI Guide (Aishwaryanr)
A one‑stop repo for GenAI research updates, notebooks, interview prep, and more. Great for staying current while practicing with solid reference materials.
GitHub – aishwaryanr/awesome‑generative‑ai‑guide
8. DMLS Book Summaries (Chi Phuyen)
Summaries and references for one of the most important ML systems books. Strengthens systems thinking, helping prevent many headaches before they begin.
GitHub – chiphuyen/dmls‑book
9. ML for Beginners (Microsoft)
A beginner‑friendly ML curriculum with practical examples and exercises you can actually finish. A solid starting point for new ML practitioners seeking quick wins.
GitHub – microsoft/ML‑For‑Beginners
10. LLM Course (mlabonne)
A hands‑on, end‑to‑end course on building, evaluating, and deploying LLM applications. Ideal when you want a clear path from spark of an idea to deployment.
GitHub – mlabonne/llm‑course
Additional Resources
For even more hands‑on repositories, explore this curated list of open‑source AI projects:
Curated List of Open‑Source AI Projects
Closing Thoughts
These resources focus on what matters: practical skills, working examples, and clear steps. Pick the repo that fits your level and goal, set a tiny deadline, and move through the materials with intent. Continue exploring AI source‑code repositories and examples to build on these projects and deepen your practice.