In one sentence
Retrieval-augmented generation is a technique where an AI model first finds relevant documents, like web pages or product data, then uses them to write its answer, so what it finds shapes what it says.
How retrieval-augmented generation works
- The shopper asks a question.
- The system searches a source, like the web or a product catalog, for the most relevant documents.
- It passes those documents to the AI model along with the question.
- The model writes an answer based on what it was given, often citing the sources.
RAG vs. a model on its own
Grounded search is RAG where the source is the live web.
Why RAG matters for ecommerce brands
With RAG, your pages are the raw material. If the retrieval step finds your page, the model can name you and get your facts right. If it finds a competitor's page instead, the answer is built from theirs.
That makes clear, specific pages and accurate structured data more important, not less.
RAG FAQs
Is RAG the same as grounded search?
Grounded search is one kind of RAG. RAG is the general technique. Grounding means the documents come from a live web search.
Do shopping assistants use RAG?
Many AI tools retrieve information before answering, especially for products, prices and stock, which change too often to rely on training alone.
How do I make my pages easy to retrieve?
Answer one clear question per section, use plain headings, keep facts up to date, and let AI crawlers read your site.
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