What is Tokenization?
The process of breaking down text into smaller units (tokens) that an AI model can process.
Deep Dive
AI doesn't read words like we do. It reads tokens. A token can be a whole word (like 'apple'), part of a word (like 'ing' in 'playing'), or even a space.
Understanding tokens is crucial for pricing (APIs charge per 1k tokens) and context windows (how much text the AI can remember).
Key Takeaways
- 1,000 tokens is roughly 750 words.
- Prices are usually quoted per 1M tokens.
- Different models use different tokenizers.
- Explains why AI sometimes struggles with spelling.
Why This Matters Now
Tokenization is the translation layer between human language and machine math. It's why AI is so efficient.
It also explains quirks. Ever notice why AI is bad at math or spelling 'reverse words'? It's because it sees the token '745' as a single ID, not the digits 7-4-5.
Common Myths & Misconceptions
One word equals one token.
Reality:No. 'Hamburger' might be one token. 'Antidisestablishmentarianism' might be 5 tokens. Complex words are split up.
Tokens are only for text.
Reality:Images are also 'tokenized' into patches (16x16 pixel squares) for Vision Transformers (ViT) to process.
Real-World Use Cases
Cost Estimation: Calculating exactly how much an API call will cost before sending it.
Context Optimization: Compressing text to fit more data into the limited Context Window.
Language Support: Creating specific tokenizers for languages like Japanese or Arabic to improve efficiency/performance.
Frequently Asked Questions
Why can't it just read letters?
Reading letter-by-letter is computationally expensive. Tokens are a compression method that makes LLMs thousands of times faster.
How do I count tokens?
OpenAI provides a 'Tokenizer' tool. In code, libraries like 'tiktoken' assume this task.
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