I Built a Free LLM Curriculum Because Every Tutorial Online Sucks — Here's What I Learned
I spent 3 months last year jumping between tutorials, YouTube videos, and blog posts trying to learn how to build with LLMs. Here's what I found:
Most tutorials are garbage. They fall into one of three categories:
The API-caller's paradise: "Just import OpenAI, call the API, you're done!" Yeah, great. You can now build a glorified chatbot. Congratulations. But what happens when your model hallucinates? What do you do when you need to fine-tune? When you need to deploy? When you need safety guardrails? Crickets.
The academic wormhole: Other resources go the opposite direction. Dense mathematical derivations of transformers. Pages of backpropagation formulas. LoRA papers. You learn the theory but have no idea how to actually use any of it. You're drowning in math and getting nowhere.
The framework prison: Then there's the tutorials that lock you into ONE ecosystem. Learn Langchain! Learn LlamaIndex! Learn Claude! But what happens when you need to switch providers? Or use a different framework? You're starting from scratch.
And worst of all? Most of these are from 2023. The LLM space moves at light speed. RAG techniques that worked a year ago are outdated. Fine-tuning methods have been replaced. And these tutorials? Still sitting there, never updated.
So I Built Something Different
I created Practical AI Engineering — an open-source curriculum that actually teaches you how to build production AI systems.
Here's what makes it different:
It goes all the way. Most tutorials stop at "call the API." This one doesn't. It takes you from understanding what a token is, all the way to shipping a monitored, production-ready AI system with safety guardrails. That's 8 phases of real progression.
Every concept has working code. Not pseudocode. Not "pretend this exists." Real, runnable Python that you can copy-paste and actually execute. Each phase ends with a capstone project — something you can actually put on your portfolio and show employers.
You pick your own path. Don't care about fine-tuning? Skip it. Want to jump straight to RAG? Go for it. The modules are clearly leveled (Beginner 🟢 / Intermediate 🟡 / Advanced 🔴) so you know exactly where you are and what's next.
Framework-agnostic means you're not locked in. Learn OpenAI? Yep. Anthropic? Claude? HuggingFace? Open-weight models? All covered. You learn the concepts, not the framework. That's real learning.
It's maintained by people who actually build AI systems. Not just educators reading papers. Practitioners who've shipped code to production, made mistakes, and learned what actually matters vs. what doesn't.
What You'll Actually Learn
- Phase 0-1: Foundations and prompt engineering (the highest ROI skill, honestly)
- Phase 2: Building real apps with APIs (your first actual product)
- Phase 3-4: RAG and fine-tuning (when prompting isn't enough)
- Phase 5-6: Agents and evaluation (how to actually measure if your AI is any good)
- Phase 7-8: Production deployment and advanced topics (the stuff that separates hobbyists from engineers)
Each phase takes 1-3 hours. Each one teaches you something concrete you can use tomorrow.
Why I'm Sharing This
This is free, MIT licensed, and open-source. I didn't build this to sell courses or gatekeep knowledge. I built it because I was frustrated, and I know I'm not alone.
The repo is actively maintained and open for contributions. If you spot outdated content, if you think an explanation could be clearer, if you want to translate it to another language — PRs welcome. This is community-powered.
Check it out here. star it to help others find it
If it helps you, star it. Not for my ego — for the algorithm. More stars = more people find it = more people learn this stuff properly instead of through garbage tutorials.
What phase are you most interested in? Drop a comment and let's discuss — or let me know if something isn't clear.