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Tokenization Process Visualization

Run the Tokenization Visualization Fullscreen

About This MicroSim

This interactive visualization demonstrates how Large Language Models convert text into tokens using subword tokenization (similar to Byte Pair Encoding). Understanding tokenization is essential for:

  • Estimating API costs (pricing is per token)
  • Managing context window limits
  • Understanding why some text uses more tokens than expected
  • Optimizing prompts for efficiency

Iframe Embedding

You can include this MicroSim on your website using the following iframe:

<iframe src="https://dmccreary.github.io/Digital-Transformation-with-AI-Spring-2026/sims/tokenization-process/main.html"
        height="702px"
        width="100%"
        scrolling="no">
</iframe>

How to Use

  1. Enter Text: Type or paste text into the input area
  2. Analyze: Click "Analyze" to see how the text is tokenized
  3. Explore Examples: Use the example buttons to see tokenization patterns
  4. Review Statistics: Check character count, word count, token count, and cost estimates

Key Tokenization Concepts

Concept Description
Subword Tokenization Words are split into smaller units based on frequency in training data
BPE (Byte Pair Encoding) Algorithm that iteratively merges frequent character pairs
Token ID Unique integer representing each token in the model's vocabulary
Context Window Maximum number of tokens the model can process at once

Token Type Legend

Type Color Description
Word Blue Complete words or word roots
Prefix Purple Common prefixes (un-, re-, pre-)
Suffix Green Common suffixes (-ing, -ed, -tion)
Number Orange Numeric values
Punctuation Pink Punctuation marks and symbols
Whitespace Gray Spaces and newlines

Tokenization Rules of Thumb

  1. ~4 characters ≈ 1 token for English text
  2. Common words are usually single tokens
  3. Rare words may be split into multiple tokens
  4. Numbers are often split digit-by-digit for large values
  5. Code tends to use more tokens than natural language
  6. Non-English text typically requires more tokens

Learning Objectives

After using this tool, students should be able to:

  • Understand (Bloom's L2): Explain how tokenization works and affects model behavior
  • Apply (Bloom's L3): Estimate token counts for different text inputs
  • Analyze (Bloom's L4): Identify why certain text patterns use more tokens

Lesson Plan

Activity 1: Token Estimation (10 minutes)

  1. Predict how many tokens different text samples will require
  2. Test your predictions using the visualization
  3. Identify patterns in tokenization

Activity 2: Cost Optimization (15 minutes)

  1. Write a 100-word prompt in verbose style
  2. Rewrite it in concise style with same meaning
  3. Compare token counts and estimate cost savings at scale

Discussion Questions

  1. Why might code use more tokens than natural language?
  2. How does tokenization affect multilingual AI applications?
  3. What are the business implications of token-based pricing?

References

  1. Sennrich, R., Haddow, B., & Birch, A. (2016). Neural Machine Translation of Rare Words with Subword Units. ACL.
  2. OpenAI Tokenizer: https://platform.openai.com/tokenizer
  3. Hugging Face Tokenizers Library Documentation

Self-Assessment Quiz

Test your understanding of the tokenization process.

Question 1: What is tokenization in the context of language models?

  1. Creating physical tokens for arcade games
  2. Converting text into smaller units (tokens) that the model can process
  3. Encrypting text for security
  4. Translating text between languages
Answer

B) Converting text into smaller units (tokens) that the model can process - Tokenization breaks text into pieces (words, subwords, or characters) that are converted to numerical IDs the model can understand.

Question 2: Approximately how many characters equal one token for English text?

  1. Exactly 1 character per token
  2. About 4 characters per token on average
  3. 100 characters per token
  4. 10 words per token
Answer

B) About 4 characters per token on average - A common rule of thumb is that ~4 characters equals approximately 1 token for typical English text.

Question 3: Why does understanding tokenization matter for API cost estimation?

  1. Tokenization has no cost impact
  2. LLM API pricing is typically based on the number of tokens processed
  3. Tokens are free
  4. Cost is only based on time
Answer

B) LLM API pricing is typically based on the number of tokens processed - Understanding how text converts to tokens helps estimate costs and optimize prompts for efficiency.

Question 4: What type of text typically requires more tokens than natural language?

  1. Short sentences
  2. Code, non-English text, and rare words
  3. Common English words
  4. Numbers under 10
Answer

B) Code, non-English text, and rare words - Code has special syntax, non-English text may use characters not well-represented in training data, and rare words may be split into multiple subword tokens.

Question 5: What is "Byte Pair Encoding" (BPE)?

  1. A method for compressing files
  2. An algorithm that iteratively merges frequent character pairs to create a tokenizer vocabulary
  3. A way to encrypt tokens
  4. A type of neural network
Answer

B) An algorithm that iteratively merges frequent character pairs to create a tokenizer vocabulary - BPE builds a vocabulary by starting with individual characters and progressively merging the most common adjacent pairs, creating subword tokens.