RAG combines retrieval over store data with generation, so the model answers from the live catalog, prices and stock instead of from memory.
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 architecture patterns behind search by meaning, answers grounded in store data and the quality of the top results in the list.
What it is
RAG combines retrieval over store data with generation, so the model answers from the live catalog, prices and stock instead of from memory.
Example in ecommerce
A shopper asks which refrigerators fit under $800: the system first retrieves the models that qualify and only then writes the consultation.
Business impact
Cuts the share of inaccurate answers and makes the assistant useful in search, on the product page and in the cart.
We will show which scenario to start with, how to tie it to metrics and where measurable results come fastest.