RAG for Search (Retrieval-Augmented Generation)
Retrieval-augmented generation (RAG) is a technique where a language model generates a response using information retrieved from an external source — such as a product catalog — rather than relying only on what it learned during training.
Part of AI Search
In an ecommerce context, RAG can power conversational or agentic search: the system retrieves relevant products from the catalog first, then uses a language model to generate a natural-language answer or recommendation grounded in those specific, current results.