RAG (Retrieval-Augmented Generation)

RAG is an AI architecture where a language model's responses are grounded by retrieving relevant documents from a knowledge base at inference time.

Web scraping is the primary way to build and update RAG knowledge bases — scrapers continuously collect fresh web content that gets embedded and indexed for LLM retrieval.