In the past, I often just jumped straight into things without thinking too much about the technical foundation.
It worked but only up to a certain point.
For my RAG Mastery Challenge, I’ve completely changed my approach:This time, I want to understand the theory from the very beginning, so I don’t run into traps later and so I can progress much faster thanks to a solid knowledge base.
In other words:No building the roof before laying the foundation.
So today was all about research:
– What exactly is RAG?
– What does Retrieval Augmented Generation really mean?
– Why is it important? What problems does it solve?
– How does it fit into modern AI workflows?
Many of you already know this deeply for me, the knowledge was only halfway complete.
So today I:
📌 watched several YouTube videos
📌 compared fundamental explanations
📌 sketched the core concepts
📌 and created my own NotebookLM learning notebook
And because I don’t want to learn just for myself but for all of us
I’m sharing the notebook here:
👉 My RAG Learning Notebook (NotebookLM)
This is where I’m collecting all learning materials that will support me along the way:Videos, explanations, sources, examples, definitions, diagrams.
If you have anything to add please let me know!I’ll include everything so we can build a powerful shared RAG learning template.
Day 2 complete.The foundation is set — tomorrow we go deeper.