Natural Language Processing
35 min read
Text Processing & Tokenization
Preparing text for AI
Before AI can understand language, text must be converted into numbers. This process—text processing and tokenization—is the foundation of all natural language processing. Get this wrong, and nothing else works.
Why Computers Need Tokens
Computers don't understand words like humans do. They work with numbers. Tokenization is the bridge: it converts text ("Hello, world!") into a sequence of numbers ([15496, 11, 995, 0]). Each number represents a "token"—a piece of text that the AI treats as a unit.
Different Tokenization Strategies
Early systems tokenized by word: each word = one token. But this fails for rare words and different languages. Modern systems use subword tokenization: common words stay whole, rare words are split into pieces. "Understanding" might be ["under", "standing"] or ["understand", "ing"]. This balances vocabulary size with coverage.
Why Tokenization Matters for You
Tokenization affects how AI "sees" your prompts. Rare words use more tokens. Some languages are less efficient. Code has its own patterns. Understanding tokenization helps you write better prompts and understand AI limitations.
💡 Key Takeaways
- Tokenization converts text to numbers for AI processing
- Modern AI uses subword tokenization for efficiency
- Token count affects context limits and costs
- Different content tokenizes differently
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