Tokenization is the foundational process that enables AI models to understand and process human language and images. It's the first step in converting raw input into a format that AI can process efficiently.
In language models, tokenization breaks down text into smaller units called tokens. These aren't just individual letters or words - they're clever combinations of characters that represent common patterns in language. For example, the word "understanding" might be split into "under" and "standing" because these patterns appear frequently in English.
Modern AI models typically process between 2,000 and 32,000 tokens at once, which is why there are limits on how much text you can input at a time. Each token is converted into a number that the AI can process, creating a mathematical representation of language.
Image generation models use a similar concept but work with visual tokens instead of text tokens. They break down images into patches - small squares that contain visual information. These patches are then converted into tokens that represent visual concepts, from simple elements like colors and shapes to complex features like textures and objects.
When generating images, the process works in reverse: the model converts its internal tokens back into visual elements, gradually building up the complete image patch by patch, guided by the text description provided.
Type your text below to see how it's broken down into tokens. This demonstrates how AI models actually "see" your input before processing it.
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