When we interact with a large language model, we usually see only the final product: a system that can answer questions, write code, summarize documents, and hold conversations. But behind that capability is a long and complex training process.

Training a modern LLM is not a single step. It is a pipeline that typically involves several stages: collecting massive datasets, pretraining the model to predict the next token, refining it with curated data, and aligning it with human preferences so that it produces helpful and safe responses.

In this chapter, we will walk through the major steps of the LLM training pipeline.