Tokenization

Tokenization is the essential first step in how AI language models process text. Before an LLM can understand or generate any content, the input text must be broken down into tokens, the fundamental units that the model reads, processes, and produces.

How Tokenization Works

The Tokenization Process

When text is fed into a language model, a tokenizer splits it into a sequence of tokens. These tokens are then converted into numerical IDs that the model can process mathematically.

Input: "Genrank optimizes your AI visibility"

Tokens: ["Gen", "rank", " optim", "izes", " your", " AI", " visibility"]

Token IDs: [5765, 11925, 19364, 4340, 634, 9552, 20742]

Types of Tokenization

Method	Unit	Example for “unhappiness”	Pros	Cons
Word-level	Whole words	[“unhappiness”]	Intuitive	Huge vocabulary needed
Character-level	Single characters	[“u”,“n”,“h”,“a”,“p”,“p”,“i”,“n”,“e”,“s”,“s”]	Tiny vocabulary	Loses word-level meaning
Subword (BPE)	Subword pieces	[“un”, “happiness”]	Balanced vocabulary	Less intuitive
SentencePiece	Language-agnostic pieces	[“_un”, “happi”, “ness”]	Works across languages	Requires training

Common Tokenization Algorithms

Byte Pair Encoding (BPE)

BPE is the most widely used tokenization method in modern LLMs, including GPT models. It works by iteratively merging the most frequent pairs of characters or character sequences in the training data.

1. Start with individual characters
2. Count all adjacent pairs
3. Merge the most frequent pair into a new token
4. Repeat until the desired vocabulary size is reached

WordPiece

Used by models like BERT, WordPiece is similar to BPE but selects merges based on the likelihood of the training data rather than raw frequency.

Unigram

The Unigram model starts with a large vocabulary and progressively removes tokens that contribute least to the overall likelihood of the training corpus.

Token Counts in Practice

Understanding token counts is important because they directly affect model usage, costs, and capabilities.

Approximate Token-to-Word Ratios

Language	Tokens per Word (avg)
English	~1.3
Spanish	~1.5
German	~1.8
Chinese	~1.5 per character
Japanese	~1.8 per character
Code	~2.5 per line of code

Real-World Token Counts

• A typical paragraph (75 words) is roughly 100 tokens
• A 1,000-word blog post is approximately 1,300 tokens
• A full-length book (80,000 words) is around 100,000 tokens
• A single tweet (280 characters) is about 50-70 tokens

Tokenization and Model Behavior

Impact on Content Understanding

The way text is tokenized can affect how well a model understands it. Common words and phrases are typically represented by single tokens, while rare or technical terms may be split into multiple subword pieces.

• Common phrase: “search engine” - 2 tokens (well-understood)
• Technical term: “crawlability” - 3-4 tokens (may be less precisely understood)
• Brand name: “Genrank” - 2 tokens (processed as subword components)

Implications for Content Creators

• Models understand frequently tokenized words and phrases more reliably
• Unusual jargon or coined terms may be split awkwardly, potentially affecting comprehension
• Widely used industry terminology is better recognized than proprietary terms

Tokenization and Cost

Most commercial LLM APIs charge per token for both input and output. This makes tokenization directly relevant to the economics of AI-powered applications.

Pricing Structure

Component	Description
Input tokens	Tokens in your prompt or query
Output tokens	Tokens in the model’s response
Embedding tokens	Tokens processed for embedding generation

Optimization Strategies

• Write concisely to reduce input token counts
• Use clear, direct prompts to encourage shorter, focused responses
• Avoid unnecessary repetition in prompts
• Choose the right model size for the task

Tokenization Across Languages

Tokenizers trained primarily on English text tend to be less efficient with other languages, requiring more tokens to represent the same content. This has practical implications for multilingual AI applications.

• English text is typically the most token-efficient
• Languages with complex morphology (like Finnish or Turkish) may require significantly more tokens
• Non-Latin scripts often require more tokens per word
• Multilingual tokenizers trade some efficiency for broader language coverage

Why It Matters for AEO

Tokenization shapes how AI models perceive your content at the most fundamental level. The way your text is broken into tokens determines how the model internally represents and reasons about your information. Content that uses clear, commonly understood language tends to tokenize more efficiently, producing representations that the model can work with more effectively.

For AEO strategy, this means that using standard industry terminology, writing in clear and direct language, and avoiding unnecessarily obscure phrasing all help ensure that AI models accurately understand your content. When a model tokenizes your page and processes those tokens through its neural network, well-tokenized content produces stronger internal representations that are more likely to be retrieved and cited accurately.

Genrank helps you understand how AI models interpret your content, providing insights into the retrieval and citation signals that begin at the token level.