How LLMs work
LLMstransformertokenizationembeddingspositional encodingattentionmulti-head attentionfeed-forward networksresidual streamsmachine learningdeep learningnatural language processing.
Author: 0xkato
Date: 6/3/2026
Article Summary:
This article is a walkthrough of how Large Language Models (LLMs) work, focusing on the core mechanisms of modern transformer-based LLMs, including tokenization, embeddings, positional encoding, attention, multi-head attention, feed-forward networks, and residual streams.