Definition in Simple Terms
Tokenization is how AI breaks text into smaller pieces called tokens so it can understand and process language.
(Example: “AI is amazing” → [“AI”, “is”, “amazing”]).
Why It Matters
AI models don’t read sentences like humans. They work with tokens. The way text is split affects accuracy, cost, and speed of AI responses.
How It Works (Step-by-Step)
Input Text: You type a sentence.
Split into Tokens: The text is broken into words or subwords.
(Example: “unbelievable” → [“un”, “believ”, “able”]).Convert to Numbers: Each token becomes an ID the model understands.
Process & Predict: The model uses these tokens to generate meaning and responses.
Reassemble: Tokens are stitched back into human-readable text.
Real-World Example
“ChatGPT uses tokenization to process your question and predict the next best token to form an answer.”
Analogy
“Think of tokenization like LEGO bricks: break a big structure into small pieces so you can build something new.”
Embedding: Turns tokens into vectors for meaning (Example: “cat” → [0.12, -0.45, 0.89]).
Subword Tokenization: Splits words into smaller chunks for better handling of rare words.
Context Window: The max number of tokens an AI can process at once.