Grounding Retail Products in Vector Databases for RAG-Driven Search

How retail products are indexed in vector databases and retrieved via Retrieval-Augmented Generation (RAG).

When a consumer asks Gemini: "Find me a durable vertical video editor package," the model does not run a SQL search. It queries a vector database where site content is represented as mathematical embeddings.

To rank in these results, your catalog data must be optimized for Retrieval-Augmented Generation (RAG). That means writing clean descriptions, eliminating linguistic noise, and using structured formats.

Understand semantic vector grounding models in our AI Commerce specification.