AI Glossaryfundamentals

Tokenization

The process of breaking text into smaller units (tokens) that an AI model can process, typically subwords or word pieces.

How It Works

Before an LLM can process text, it must be converted to tokens. A token is roughly 3/4 of a word in English. "Tokenization" becomes ["Token", "ization"]. This matters because API pricing is per-token, context windows are measured in tokens, and different tokenizers produce different token counts for the same text. GPT-5 uses ~100K token vocabulary; understanding tokenization helps optimize costs and context usage.

Common Use Cases

  • 1API cost estimation
  • 2Context window management
  • 3Multilingual processing
  • 4Text preprocessing

Related Terms

Large Language Model (LLM)

A neural network trained on massive text datasets that can generate, understand, and reason about human language.

Context Window

The maximum amount of text (measured in tokens) that an AI model can process in a single request, including both input and output.

Embeddings

Numerical vector representations of text that capture semantic meaning, enabling similarity search and clustering.

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