Most devs don't understand how LLM tokens work

The podcast elucidates the concept of tokens as the fundamental currency within Large Language Models (LLMs). It explains how LLMs process text by breaking it down into tokens, which are numerical representations of words, subwords, or characters, drawn from a specific vocabulary. Different models utilize distinct token vocabularies, leading to variations in token counts for the same input text. The process of training tokenizers involves identifying frequently occurring character groups within a large text corpus to optimize vocabulary size and processing efficiency. The podcast uses TypeScript code examples to illustrate token encoding and decoding, and demonstrates how less common words result in a higher token count.