How LLMs Actually Work

This post covers the core mechanisms inside modern transformer-based LLMs, including tokens, embeddings, positional encoding, attention, multi-head attention, and more. Tokenization converts text into integer IDs, embeddings give tokens meaning through vectors, and positional encoding helps the model understand the order of tokens. Attention allows tokens to share information with each other, and multi-head attention tracks different relationships simultaneously.