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How does retrieval-augmented generation work?
- Question. A customer asks something, such as “Is the travel size back in stock?”
- Retrieval. The system searches trusted sources, catalogue, stock levels, policies, help articles, for relevant information.
- Generation. The language model writes a natural reply using that retrieved information as its basis.
- Fallback. If nothing relevant is found, a well-designed system says so or hands off, rather than guessing.
Why does RAG matter for ecommerce?
A large language model on its own knows general language and facts from its training data, but nothing about your store today. It doesn’t know your returns window, this morning’s stock levels or a customer’s order. Retrieval supplies that context at the moment it’s needed, so answers reflect your business, not the model’s assumptions.
RAG also makes updates simple: change a policy or product description, and answers change with it, without retraining a model.
What are its limits?
RAG is only as good as its sources. Out-of-date or contradictory help content produces out-of-date or contradictory answers. Keeping policies and product information clean is part of running AI well.
How Yep AI uses it
Yep AI Agents ground answers in your catalogue, policies and order data through one unified Shopify data layer, so replies reflect your store as it is right now. That grounding is one of the guardrails described in AI Agent Security.
