Token

Also known as: Text Token, Language Token, Word Piece

The basic unit of text processing in language models, representing parts of words, whole words, or punctuation.

The basic unit of text processing in language models, representing parts of words, whole words, or punctuation.

What is Token?

A token is the fundamental unit of text that language models process, representing fragments of text that may be parts of words, complete words, punctuation marks, or special characters. Tokenization is the process of breaking text into these units based on specific rules. In English, tokens often correspond roughly to 4 characters or 3/4 of a word on average, though this varies widely depending on the specific tokenization algorithm and language.

Why It Matters

Understanding tokens is essential for AI optimization because they directly impact cost, performance, and capabilities of language models. Token limits constrain context windows, affecting how much information can be processed at once. Optimizing content for efficient tokenization can reduce costs and improve model performance, especially for applications with high volume or requiring extensive context.

Use Cases

Content Optimization

Structuring text to minimize token usage while preserving meaning.

Cost Management

Estimating and controlling API costs based on token usage.

Context Planning

Designing prompts and documents to fit within token limits.

Optimization Techniques

To optimize token usage, use common words where possible (they often tokenize more efficiently), avoid unnecessary repetition, and structure information concisely. For technical content, consider that specialized terminology and code may tokenize less efficiently. When working with context limits, prioritize the most relevant information and consider chunking strategies for large documents.

Metrics

Measure tokenization efficiency through tokens per character ratio, tokens per word ratio, and total token count for equivalent content expressed different ways. For applications, track token usage per request, cost per useful output, and context utilization percentage.

LLM Interpretation

Language models process text by converting it into tokens, which are then transformed into numerical embeddings. The model's understanding of language is built upon these token-level representations and their relationships. Token boundaries affect how models interpret text, with some tokenization choices potentially impacting the model's perception of word meanings and relationships.

Code Example

// Example of counting tokens in JavaScript
async function countTokens(text) {
  // Using a tokenizer library
  const tokenizer = new GPT3Tokenizer({ type: 'gpt3' });
  const encoded = tokenizer.encode(text);
  return encoded.bpe.length;
}

// Example usage
const text = "This is an example sentence to count tokens.";
const tokenCount = await countTokens(text);
console.log(`The text contains ${tokenCount} tokens.`);

Related Terms

LLMs.txt

A standardized file that provides instructions to AI crawlers about how to interpret and use website content.

Embeddings

Numerical representations of text, images, or other data that capture semantic meaning in a high-dimensional space.

Prompt Engineering

The practice of designing and optimizing inputs to AI systems to elicit desired outputs or behaviors.

Structured Data

{
  "@context": "https://schema.org",
  "@type": "DefinedTerm",
  "name": "Token",
  "alternateName": [
    "Text Token",
    "Language Token",
    "Word Piece"
  ],
  "description": "The basic unit of text processing in language models, representing parts of words, whole words, or punctuation.",
  "inDefinedTermSet": {
    "@type": "DefinedTermSet",
    "name": "AI Optimization Glossary",
    "url": "https://geordy.ai/glossary"
  },
  "url": "https://geordy.ai/glossary/ai-fundamentals/token"
}

Term Details

Category
AI Fundamentals
Type
concept
Expertise Level
beginner
GEO Readiness
structured