The AI Engineering Roadmap I Wish I Had
A practical 2026 guide with videos, books, courses, projects, and resources to become a real AI engineer from scratch.
Good morning!
Quick note before anything else: there might not be a new video this week.
I’m doing my best to get one out by the end of the week, ideally Sunday, but I don’t want to promise it if it’s not ready. I’d rather publish something useful than rush something just to keep the schedule.
So instead, I wanted to share something I’ve been working on for a little while for you (you are the first to see it!):
My full Start AI Engineering in 2026 roadmap.
You can find it here: https://github.com/louisfb01/start-ai-engineering
This is basically the AI engineering version of my old Start Machine Learning repo, but updated for what the field actually looks like now.
And the field changed a lot.
Learning AI engineering in 2026 is not just “learn prompting” or “build a chatbot.”
It’s learning how to make decisions around:
when to use prompting, RAG, fine-tuning, workflows, or agents
when not to use an LLM at all
how to evaluate outputs
how to debug failures
how to trace systems
how to deploy and monitor them
how to use coding agents without outsourcing your thinking
That last point is important.
Codex, Claude Code, Cursor, Gemini CLI, and all these tools make it easier than ever to build something that looks impressive.
But building faster is not the same as understanding what you built.
And this is the main reason I made the repo.
I wanted a guide for people who want to become real AI engineers, not just people who can ship fragile AI demos with an agent.
The repo includes:
beginner Python resources
foundational AI and LLM videos
books I recommend
free and paid courses
RAG, agents, evals, MCP, deployment, safety, and coding-agent resources
project ideas
communities
newsletters
people to follow
job-search advice
difficulty levels from 1️⃣ to 🔟 for each resource
I also added a suggested path so you don’t just stare at 300 links wondering where to start, like most LinkedIn posts and listicles out there.
The rough flow is (optionally):
Learn the basics and vocabulary (usually with YouTube videos).
Pick one free course and one framework.
Pick one or two books for stronger foundations.
Optionally take one or two applied courses with real projects.
Build small systems that break. (repeat)
Add evals, tracing, and deployment before pretending it is production-ready.
OR, build your custom curriculum by giving the repo link to Claude/Chat and telling it your learning preferences. Nobody learns the same way! I added a prompt inside the repo that you can paste into your AI agent to turn the roadmap into a personalized learning plan based on your background, time, budget, and goals.
That’s the real learning.
Not just watching videos. Not just collecting bookmarks. Not just asking an agent to build everything.
Building, breaking, fixing, and understanding why.
So yes, you can use AI to learn AI engineering.
You probably should.
But the goal is still to build your own judgment.
Here’s the repo again:
https://github.com/louisfb01/start-ai-engineering
If you find it useful, please star it and share it with one person who wants to get into AI engineering this year.
That helps a lot and tells me whether I should keep improving it.
Hope you find this useful!
Speak soon (hopefully this Sunday!),
p.s. If you see a great resource missing, feel free to reply to this email with it. I want this to become one of the most useful starting points for AI engineers in 2026.