Back to Glossary
AI / ML
RAG
A technique where an LLM retrieves real-time data from external sources (product databases, review sites, brand websites) before generating a response. This is how AI shopping agents pull current pricing, availability, and product specifications rather than relying solely on training data. Brands with well-structured product data and schema markup are more easily retrieved by RAG systems, leading to more accurate and favorable agent recommendations.
Why it matters
It is how an agent answers from your data rather than from what it absorbed in training, which is the difference between a specific answer and a plausible one.
In practice
Retrieval quality sets the ceiling. A well-prompted model over the wrong documents produces a confident wrong answer.
Related terms