# Tokenization

Tokenization is the way an AI language model splits text into smaller units it can process.

## Why it matters

Models process tokens rather than words directly. Tokenization affects context usage, cost, latency, and how text in different languages or formats is represented; token counts are model-specific.

## Example

A team checks token counts for multilingual support documents before setting chunk sizes and budgets for its retrieval pipeline.

## FAQ

### What is Tokenization in simple terms?

Tokenization is the way an AI language model splits text into smaller units it can process.

### Why does Tokenization matter for teams using AI?

Models process tokens rather than words directly. Tokenization affects context usage, cost, latency, and how text in different languages or formats is represented; token counts are model-specific.

### What is a practical example of Tokenization?

A team checks token counts for multilingual support documents before setting chunk sizes and budgets for its retrieval pipeline.


Source: https://www.luffy.so/ai-glossary/tokenization

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