What Tokenization Is
Tokenization is the process of splitting raw text into subword units that a language model can process.
Models do not read words. They read token IDs.
Why Tokenization Exists
- Enables handling of unseen words
- Reduces vocabulary size
- Optimizes training efficiency
How Tokenization Works
Most modern LLMs use Byte Pair Encoding (BPE) or similar subword algorithms.
The process:
- Start with a base vocabulary of characters
- Iteratively merge the most frequent adjacent pairs
- Build a vocabulary of common subword units
- Encode any text as a sequence of these units
Example
Input text:
Understanding embeddings
Tokenized output:
["Understand", "ing", " embed", "dings"]
Notice how common suffixes like "ing" become separate tokens. This is intentional. It allows the model to recognize patterns across different words.
Token Counts Are Not Word Counts
A single word can produce multiple tokens. A common phrase might produce fewer tokens than expected.
Examples:
| Text | Approximate Tokens |
|---|---|
| "Hello" | 1 |
| "Understanding" | 2 |
| "Anthropomorphization" | 4+ |
| "GPT 5.2" | 3 |
What This Means for Your Content
Tokenization has limited direct impact on whether AI cites your brand. You cannot control how AI systems tokenize your website.
But understanding tokenization reveals something critical:
AI does not read your content the way humans do.
When someone visits your website, they scan headlines, absorb tone, interpret visual hierarchy. AI skips all of that. It converts your text into numerical sequences and processes patterns.
This is why:
- Brand voice doesn't translate to AI understanding
- Clever wordplay often fragments into meaningless tokens
- Simple, direct language survives the pipeline intact
The real impact happens downstream. Tokenization feeds into embeddings, which determine whether your content gets retrieved. If your language is clear at the token level, it has a better chance of producing stable embeddings.
Practical Takeaway
Write like you're explaining something to a smart colleague, not like you're crafting ad copy.
- Use common vocabulary that appears frequently in professional writing
- State things directly instead of through metaphor
- Avoid internal jargon that AI has never encountered
This isn't about gaming tokenization. It's about recognizing that AI processes language mechanically, and mechanical processes reward clarity.
Key Takeaway
Tokenization is mechanical, not semantic — but it constrains everything that follows.
The model's first interaction with your content happens at the token level. Before meaning, before understanding, there are just numbers.