What is Tokenization?
Tokenization splits text into subword units (tokens) so that LLMs can process it. Tokens are the basic accounting unit for context window limits and pricing. Understanding tokenization helps you estimate costs and structure prompts and retrieval.
How does Tokenization work?
Most modern tokenizers use algorithms like BPE or WordPiece. They map text to token IDs, which the model consumes. Different models have different tokenizers and tokenization quirks, affecting length and behavior.
When should you use it? (Typical use cases)
- Cost estimation and budgeting for LLM calls.
- Prompt design that fits the context window.
- Consistent chunking for retrieval pipelines.
- Measuring content drift and compression needs.
Benefits
- Predictable costs
- Fewer truncations
- Better retrieval quality
Common pitfalls/risks
- Tokenizer mismatch across models
- Unexpected splitting of non-English/technical text
Antire and Tokenization
We model token usage, optimize prompts, and design chunking strategies for RAG so you stay within latency and budget targets.
Services
Data platforms and applied AI
Tailored AI & ML
Cloud-native business applications
Fast Track AI Value Sprint
Related words:
Large Language Model (LLM)
LLMOps
LLM evals (evaluations)