Explore how different LLMs break down text into tokens
Tokenization is the process of breaking down text into smaller units called tokens. Different AI models use different strategies:
Word-based: Splits on spaces and punctuation. Simple but creates huge vocabularies.
Subword-based: Breaks words into smaller meaningful parts. More efficient and handles unknown words better.
Character-based: Uses individual characters. Very fine-grained but requires longer sequences.
Modern LLMs like GPT, Claude, and LLaMA use sophisticated subword algorithms like Byte Pair Encoding (BPE) and SentencePiece to balance vocabulary size with meaningful representation.
Why this matters: Token count affects API costs, context limits, and model performance. Understanding tokenization helps optimize your AI applications!