Tokenization
The process of breaking text into sub-word pieces that AI models can process mathematically — determining what fits in the model’s “reading window.”
Plain Language Definition
Tokenization is how an AI breaks your text into tiny sub-word pieces it can process mathematically. The word “optimization” might become three tokens: “optim,” “iz,” “ation.” This matters because AI models have context window limits measured in tokens, and how your content tokenizes affects how much of it can fit in the retrieval and generation pipeline at once.
Technical Definition
Mathematical parsing of raw text input into discrete integer-mapped character sequences using algorithms like Byte-Pair Encoding (BPE) — the preprocessing step that converts human-readable text into the numerical input format processed by transformer models.
Why This Matters for AI Search Visibility
Understanding tokenization explains context window limits, why very long pages may only be partially read by AI systems, and why clean, non-redundant prose uses tokens more efficiently. Content that packs more meaning per token is retrieved and processed more effectively.