Tokenization and Embeddings: The Language of LLMs in Generative AI
Tokenization and Embeddings: The Language of LLMs
Before a model understands text, it must convert words into numbers. This conversion happens through tokenization and embeddings.
1) What is Tokenization?
Tokenization splits text into smaller units called tokens.
- Word-level tokens
- Subword tokens
- Character tokens
Modern LLMs use subword tokenization (like BPE) to handle rare words efficiently.
2) What are Embeddings?
Embeddings convert tokens into dense numerical vectors. These vectors capture semantic meaning.
For example:
King - Man + Woman ≈ Queen
This works because embeddings capture relationships in vector space.
3) Why Embeddings Matter in Production
- Used in semantic search
- Power RAG systems
- Enable clustering and similarity comparison
4) Summary
Tokenization converts text to tokens. Embeddings convert tokens into meaning. Together, they allow models to process language mathematically.
