This article explains how Large Language Models (LLMs) process prompts from tokenization to response generation. It covers the transformer architecture, including self-attention and feed-forward networks, and details the importance of the KV cache in optimizing performance.

The article discusses the limitations of tokenization in large language models (LLMs) and argues for a shift towards more general methods that leverage compute and data, in line with The Bitter Lesson principle. It explores potential alternatives, such as Byte Latent Transformers, and examines the implications of moving beyond traditional tokenization approaches, emphasizing the need for improved modeling of natural language.