---
title: What is retrieval-augmented generation (RAG)? Meaning & examples
description: Retrieval-augmented generation (RAG) lets an AI answer from your
  own documents and catalog instead of guessing. Learn how it works and why
  sales chats need it.
url: https://salemag.net/glossary/retrieval-augmented-generation
language: en
published: 2026-10-21
updated: 2026-10-21
author: SaleMag Team
tags:
  - rag
  - ai chatbot
  - knowledge base
---

# What is retrieval-augmented generation (RAG)? Meaning & examples

> Retrieval-augmented generation (RAG) is a way of building AI assistants where the system first searches your own content, like FAQs, policies and product data, for the passages that match a question, then gives only those to the language model to write the answer. It keeps replies grounded in your facts.

## What is retrieval-augmented generation?

**Retrieval-augmented generation**, or **RAG**, is a method for making an AI assistant answer from *your* information rather than from what the model happened to learn during training. It works in two steps:

1. **Retrieve.** Your documents (FAQ, return policy, delivery charges, product descriptions) are split into small chunks and indexed, usually as embeddings that capture meaning. When a customer asks a question, the system finds the chunks that best match it.
2. **Generate.** Those chunks are given to the language model along with the question, and the model writes a reply based on them.

If nothing relevant is found, a well-built system says so or passes the chat to a person instead of inventing an answer.

## Why does it matter for sellers in Bangladesh?

A general AI model knows nothing about your delivery charge outside Dhaka, your exchange policy or which courier you use. Without RAG it may guess, and a wrong promise in chat turns into a return or a bad review. With RAG you upload your own policies once and update them when they change, and the [AI agent](https://salemag.net/glossary/ai-agent) answers from them.

## Example

A customer asks, "Exchange korte parbo? Size boro hoye gele?". The system retrieves the exchange-policy paragraph ("size exchange within 3 days, unworn, with tag") and the AI answers from it, in the customer's language.

In SaleMag's [AI chatbot](https://salemag.net/features/ai-chatbot), your knowledge-base documents are split into chunks and searched for each question, and only chunks above a relevance cutoff reach the AI. When it cannot answer from that material, it can hand the chat to your team.

## Related terms

AI agent, [human handover](https://salemag.net/glossary/human-handover), Banglish.

## FAQ

### Does RAG stop an AI from making things up?

It reduces it a lot, because the model is told to answer from the retrieved text. A good setup also tells the AI to hand over to a person when nothing relevant is found.

### What content should I add to a knowledge base?

Delivery areas and times, return and exchange policy, payment options, size guides, opening hours and answers to the questions customers ask most.

### Do I need to retrain the AI when my prices change?

No. With RAG you update the documents or product data, and the next answer uses the new information.