Tokens Explained
Summary
Tokens aren't words—a single word like 'unbelievable' counts as three tokens in AI models. Since every API call charges by tokens and context windows are token-limited, understanding token economics is critical for managing AI costs and preventing performance degradation mid-conversation.
Key Takeaways
- One token ≠ one word. Complex words like 'unbelievable' consume 3 tokens ('un', 'believe', 'able'), directly impacting API costs and efficiency calculations.
- Token limitations cause real performance issues. AI conversations degrade when models run out of tokens, appearing to 'get worse' mid-interaction due to context window constraints.
- Token management is a cost and speed bottleneck. Every AI API call has speed limits and context window constraints directly tied to token allocation—critical for product decisions.
- Monitor token consumption in production. Track when AI agents lose performance mid-conversation to identify token exhaustion and optimize context window usage.
Related topics
Transcript Excerpt
Today we're breaking down tokens. Ah, yes, tokens. My sweet, sweet tokens. Tokens are the currency of AI models. So, what is a token, you may ask? Well, a token is a chunk that an LM is able to read and write with. A common misconception is that one token is equal to one word, but that is actually not the case. For example, with the word unbelievable, this is actually three tokens: un, believe, and able from the AI model's perspective. So, why does this even matter? Because every AI costs speed [music] limit and context window issue is really a token limitation. That's also why sometimes when you're in the middle of a conversation with an AI agent, it just suddenly feels like it got worse. That probably has something to do with the fact that it's running out of tokens, to put it simply. In…