Train LLM from Scratch
This guide walks through building and training a large language model (LLM) from scratch, covering architecture design, data preparation, and training procedures.
Background
This is a technical guide for building a large language model (LLM) — the kind of AI that powers ChatGPT, Claude, and Gemini — from the ground up, meaning from raw code rather than fine-tuning an existing model. The author, Fareed Khan, walks through every stage: collecting training data, designing the neural network architecture (the "transformer" that underpins modern LLMs), training the model on that data, and then running inference (getting it to generate text). For readers following AI development, this matters because most discussion treats LLMs as black boxes; a from-scratch tutorial demystifies what's actually inside them — the tokenization, the attention mechanism, the backpropagation — and makes concrete what "training compute" really means. It also helps distinguish genuine capability from hype: if you know how one is built, you can better judge what's impressive vs. what's just a bigger dataset.