What Is a Token in AI? Tokenization Explained for Developers
An AI model never reads your sentence the way you do. Before it sees anything, your text is cut into tokens and turned into numbers. Tokens are what the model actually works in, and they are what you are billed for.
This video follows one example all the way through: a Rails app sending a customer support message to an AI API, and what the token count does along the way.
What you will learn:
- What a token actually is, and why it is not a word
- Why a long word splits into several tokens
- Why whitespace and punctuation are tokens too
- What a tokenizer does, and where it sits in the request
- How tokens map to numbers in the model's vocabulary
- Why both halves count: input tokens and output tokens
- How a Rails app can read token usage back from the response
- What tokens give you: context management, usage monitoring, cost estimation
- What to watch: tokenization, context limits and pricing all vary by model
Chapters:
00:00 - The hook: AI does not read words
00:10 - The problem: your app speaks text, the model does not
00:25 - The concept: one word is not one token
00:40 - How it works: text, tokens, numbers, model, text
01:00 - A practical Rails example
01:25 - Benefits and trade-offs
01:45 - The key takeaway
A note on the numbers in this video: the token splits, the ID numbers and the usage counts are all illustrative, and they are labelled as such on screen. Real token boundaries depend on the tokenizer, real vocabulary numbers depend on the model, and real usage depends on your prompt. Count with the model's own tokenizer rather than estimating from characters or words.
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