How RAG works
A RAG system searches an external knowledge base — often a vector database of document embeddings — for content relevant to a user’s query, then feeds that retrieved content to the language model alongside the question to generate a grounded answer.
Why fresh web data matters for RAG
RAG systems are only as current as the data they can retrieve. Continuously updated, well-structured web data feeds are what let a RAG-based application answer questions about recent prices, listings, or events accurately.
