How to Optimize Token Usage in Claude Code
AI Summary
In this video, the presenter explains how large language models (LLMs) calculate costs based on token usage, detailing the distinction between input and output tokens. The video emphasizes that LLMs break down text into tokens, which affects API pricing. The presenter shares tips on optimizing token usage to reduce costs, such as starting new chats for separate tasks, summarizing longer chats, and selecting the appropriate model based on the task’s complexity. Specifically, he highlights the importance of avoiding long conversation threads and suggests practical commands to clear history and manage token efficiency. The video aims to help users maximize their use of LLMs while minimizing expenses.