Glossary

Retrieval-Augmented Generation (RAG)

In one line: RAG lets an AI assistant pull real answers from your actual business data instead of guessing from its training memory.

The problem RAG solves

A vanilla AI assistant only knows what was in its training data — it doesn’t know your products, your prices, your policies, or your order history. Ask it “what’s your return policy” and it’ll either make something up or refuse to answer. Neither is good.

How RAG works

RAG connects the AI to your real data. When a customer asks a question, the system first searches your knowledge base (product catalog, support docs, order database) for relevant information, then feeds those snippets to the AI along with the question. The AI generates an answer grounded in the actual data — not its memory.

Why it matters for retail

Almost every useful AI deployment in retail uses RAG under the hood:

• Customer support — answers from your real product info and policies
• Sales chat — recommendations from your real inventory
• Internal Q&A — your team queries internal docs in plain English
• Order assistance — pulls live order data, not invented information

What you need

Your business documents (FAQs, product info, policies) in a structured format. Most modern RAG setups handle PDFs, docs, spreadsheets, and database connections. The cleaner and more current your source data, the better the answers.

Related terms

Large Language Model (LLM), Hallucination, Chatbot vs AI Assistant