What is tokenization in LLMs?

Definition: Tokenization is the step that splits text into tokens, the word pieces a language model actually reads and writes, and it determines how much text fits in a context window and how usage is billed.

How tokenization works

A tokenizer maps text to integer IDs from a fixed vocabulary, usually built with algorithms such as byte-pair encoding. Common words often become one token, while rare words, code and many non-English languages split into several. Each model family has its own tokenizer, so the same sentence can produce different token counts across providers.

Why it matters

Tokens are the unit for context window limits, latency and API pricing, which is usually quoted per million input and output tokens. Token counts also explain some odd model behavior, such as trouble counting letters in a word, because the model sees pieces of words rather than characters.

How teams use it

Teams count tokens before sending requests, trim or summarize long inputs, size document chunks in tokens rather than characters, and track token usage per feature so they can forecast and control cost.

How Ryz helps

Ryz LLM engineers build token budgeting and cost tracking into production AI applications, working on your team and reporting to your leads.

Related

• LLM development services
• LLM engineer rates
• Large language model
• Context window