BuildFuture.AI
What are Tokens in AI?
Basic Units of Text: Tokens are the smallest meaningful units into which text data is broken down before being fed into an AI language model.
Not Always Complete Words: Tokens can be: Whole words (e.g., "the", "cat", "run") Parts of words (e.g., prefixes and suffixes like "un-" and "-able") Special characters (e.g., punctuation, symbols that carry meaning)
Machine Understanding: AI models can't process raw text like humans do. Breaking language into tokens allows them to recognize patterns and relationships between words (or parts of words).
Handling Complexities of Language: Tokenization helps manage: Different word forms: ("running" and "ran" might be tokenized into "run" + a suffix) Phrases: ("New York" might be a single token) Out-of-vocabulary words: Breaking them into known smaller units for analysis
– Word-level Tokens – Subword Tokens – Character-level Tokens – Special Tokens
Consider the sentence: "The quick brown fox jumped." Word-level tokenization: ["The", "quick", "brown", "fox", "jumped", "."] Subword tokenization: Might split "jumped" into ["jump", "##ed"] to recognize the past tense.
Tokenization is a crucial step in natural language processing (NLP) tasks like: Machine translation Text summarization Chatbots Content generation