RAG (Retrieval-Augmented Generation)
The architecture most AI answer engines use — retrieve live web content, then generate an answer by combining retrieved facts with model knowledge.
What it means
RAG is the technical method behind Perplexity, ChatGPT Browse, Bing Copilot, and Google's AI Overviews. Instead of answering purely from training data, the model performs a live web retrieval, selects relevant passages, and uses those passages as grounding for its generated answer. For merchants, this means fresh, crawlable, well-structured content on their live site can influence AI answers in near-real-time — you don't have to wait for model retraining. A Shopify store with regularly updated, crawlable product content feeds directly into the RAG pipelines used when AI platforms answer current buyer queries.
Why it matters for Shopify
Because of RAG, Shopify merchants can influence AI citations within hours of updating their product pages — no waiting for model updates.