LLM Foundations
A guided route from raw text to a working mental model of how large language models are trained and used.
Who this is for
You know some Python and a little ML, and you want a clear order to learn how modern LLMs work — without jumping randomly between blog posts.
How to use this path
Work top to bottom. Each step builds on the last: you can't reason about RAG until attention clicks, and attention is easier once tokenization makes sense. Spend real time on step 2 — it's the keystone.
What you'll be able to do
By the end you'll be able to explain, in plain language, how a prompt becomes a prediction, why context windows matter, and when to reach for retrieval versus fine-tuning.