What are Tokens
The text units, such as words, subwords, or characters, that language models read and generate.
Definition
Tokens is the text units, such as words, subwords, or characters, that language models read and generate. In practical AI work, it helps teams connect a concept to data, model behavior, product choices, evaluation, and risk. The useful question is not only what the term means, but how it affects quality, cost, reliability, safety, and decisions in a real workflow.
Example
An AI workflow uses Tokens to organize knowledge, choose actions, or solve a structured problem.
Why it matters
Tokens matters because the text units, such as words, subwords, or characters, that language models read and generate can change how teams build, evaluate, choose, or govern AI systems. It gives teams a clearer way to reason about AI behavior, choose system designs, and explain what a tool can or cannot do.
How it works
The concept is usually modeled through inputs, states, rules, representations, search, or learned behavior, then checked against the task the system must solve. For Tokens, the key is to connect the definition with inputs, assumptions, measurable outcomes, and deployment limits.
Where it is used
- Used in AI product design, automation, agents, planning, knowledge systems, robotics, simulation, and research workflows.
Limitations
A formal definition may not tell whether a tool works well in a real workflow; testing on realistic data is still necessary.