// GLOSSARY -- AGENTIC AI

What is Tokenization (AI)?

1 min read Updated

Tokenization in AI refers to breaking text into smaller units (tokens) that a language model can process — typically subword pieces that balance vocabulary size with representation efficiency.

WHY IT MATTERS

Before an LLM can process text, it must be tokenized — converted from characters into numerical token IDs. Most models use subword tokenization (BPE or SentencePiece), splitting text into common subword units.

Tokenization affects cost (you pay per token), context window usage, and model capabilities. Different tokenizers handle numbers, code, and non-English text differently.

Not to be confused with crypto tokenization (creating digital tokens on a blockchain), AI tokenization is a technical detail that developers should understand for cost optimization.

FREQUENTLY ASKED QUESTIONS

How many tokens is a typical word?
In English, roughly 1 token per word on average. Common words are single tokens; technical terms often split into multiple tokens. Rule of thumb: 1 token ≈ 4 characters.
Why does tokenization matter for costs?
LLM APIs charge per token. Understanding tokenization helps you estimate costs, optimize prompts, and manage context windows efficiently.
Is AI tokenization related to crypto tokens?
No. They share the word 'token' but are completely different concepts. AI tokenization splits text for processing; crypto tokenization creates digital assets on a blockchain.

FURTHER READING

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