What is Tokenization? - Definition & Meaning
Learn what tokenization is, how text is split for AI models, and why tokenization is crucial for LLMs and language processing.
Definition
Tokenization is the process of splitting text into smaller units (tokens) — words, subwords, or characters — that an AI model can process. Tokens are the basic unit for input and output of language models.
Technical explanation
Tokenization determines how text is represented. Word-level tokenization splits on spaces but fails on unknown words. Subword tokenization (BPE, WordPiece, SentencePiece) splits into common subunits, avoiding out-of-vocabulary issues. Each token receives a numerical ID from the vocabulary. Token limits (e.g., 128K for GPT-4) cap context length. Different languages and scripts yield different token counts. Tokenization affects cost (API pricing per token) and quality.
How AVARC Solutions applies this
AVARC Solutions considers tokenization when designing AI solutions. We choose tokenizers that support Dutch and domain-specific terminology well, optimize context lengths for RAG and chatbots, and monitor token usage for cost control.
Practical examples
- A chatbot supporting a 2000-token prompt so sufficient conversation context can be passed without exceeding the limit.
- A document analysis pipeline splitting long PDFs into 512-token chunks for embedding and retrieval.
- A translation service monitoring token usage to predict and optimize API costs.
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