## Definition
Tokenization in AI refers to breaking text into smaller units, called tokens, which are words or subwords. These tokens form the basis for text processing in NLP and AI models.
## How It Works
Tokenization converts sentences into numerical representations. Each token is mapped to an ID and processed by models like transformers for context understanding.
## Examples or Use Cases
Used in models like GPT, tokenization allows understanding complex inputs, translation, and text generation.
## Related Terms
– [Embedding](#)
– [Fine-tuning](#)
– [Transformer Model](#)
## Summary
Tokenization is fundamental to AI language models, allowing them to interpret text structures efficiently.
Tokenization in AI – Definition and Use Cases