RAG (Retrieval-Augmented Generation)

An AI pattern where the chatbot first retrieves the most relevant facts from your store (products, policies, FAQs) and then generates the answer grounded in those facts. Prevents hallucinations and keeps responses tied to your real catalog.

In depth

Retrieval-Augmented Generation (RAG) is the standard architecture for AI chatbots that need to answer accurately from a specific knowledge base. In e-commerce, that's your catalog and policies. Rather than asking the language model to recall facts from training, the system first runs a search to retrieve the relevant products, pages, or FAQs, then passes those retrieved snippets to the model alongside the shopper's question. The model's answer is constrained by what was retrieved, which is why RAG-based chatbots can cite specific products and prices instead of making them up. Emporiqa is built on RAG: every shopper turn triggers a hybrid retrieval over your synced data (products + policies + pages), and the language model only sees what was retrieved. The retrieval is what makes 'closes sales' possible: the model recommends real SKUs at real prices.