Tokenization
The process of breaking text into smaller units (tokens) that a language model can process.
Tokenizers split text into subword pieces that the model treats as its vocabulary. A single word might become one or several tokens depending on the tokenizer. Token counts determine both processing cost and context window usage.
Understanding tokenization is essential for cost management and chunk sizing. Languages with non-Latin scripts often require more tokens per word, and specialized terminology may tokenize inefficiently, affecting both cost and the amount of context that fits in a single request.
Related Terms
More ai/ml Terms
Retrieval-Augmented Generation (RAG)
An AI architecture that combines information retrieval with text generation to produce answers grounded in source documents.
Vector Embedding
A numerical representation of text as a high-dimensional vector, enabling semantic similarity comparisons between passages.
BM25
A probabilistic keyword-ranking algorithm that scores documents by term frequency and inverse document frequency.
Chunking
The process of splitting large documents into smaller, overlapping segments optimized for retrieval and embedding.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
Large Language Model (LLM)
A neural network trained on massive text corpora that can understand and generate human language.
Analyze Documents Related to Tokenization
Upload any document and get AI-powered analysis with verifiable citations.
Start Free