What is the concept of Token in AI
A token represents the smallest unit of text processed by an AI model, analogous to words or word segments. It is the fundamental building block upon which language models operate, enabling them to interpret and generate human language.
Tokens are created from raw text through a process called tokenization. Different tokenization methods exist, splitting text based on spaces, rules, or algorithms optimized for language patterns. The specific count of tokens varies significantly across models; common words may be single tokens, while complex words or punctuation are often split. Importantly, model inputs and outputs, along with context lengths, are measured and constrained in tokens rather than characters or words.
Understanding tokens is crucial for efficient AI interaction. They dictate computational cost, impact response length limits, and influence how prompts are processed. Optimizing token usage helps manage costs and ensures prompts fit the model's context window, directly affecting the relevance and quality of the AI's output.
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