Tokens are the units of text a model measures input and output in: they drive context limits, response speed and the cost of processing.
How to use the term
What to read alongside
This term is worth reading together with the neighbouring concepts in its section and the Gravity AI launch scenarios it belongs to.
Glossary section
The basics you need to discuss an LLM product seriously: how a model reads a request, why context matters and where controllable answers come from.
What it is
Tokens are the units of text a model measures input and output in: they drive context limits, response speed and the cost of processing.
Example in ecommerce
Blending too many product descriptions into an answer makes the conversation slower and more expensive, so catalog context has to be assembled selectively.
Business impact
Drives cost per session, latency and how far the scenario scales as traffic grows.
A fit for terms about search by meaning, the architecture behind the results and product discovery.
Shows how AI terms turn into a real decision flow inside the product page.
Collects the product scenarios in one place and is the starting point for a demo.
We will show which scenario to start with, how to tie it to metrics and where measurable results come fastest.