# What is RAG (retrieval-augmented generation)?

> Retrieval-augmented generation (RAG) lets an AI model answer from your own information: when a question comes in, the system first retrieves the most relevant passages from your documents or databases, then gives them to the model to write the answer — so replies are grounded in approved, up-to-date content instead of the model’s memory.

By Vijay Sharma, Agentic AI Tech Hub · Updated October 8, 2026 · https://www.agenticaitechhub.com/blog/what-is-rag

## How does RAG work?

1. **Prepare:** split documents into passages and index them (often as embeddings in a vector database).
2. **Retrieve:** find the passages most relevant to the question.
3. **Generate:** the model answers using those passages, ideally citing them.

## Why do businesses use RAG?

- Answers based on **your** policies, products and documents.
- Easy to update — change the document, not the model.
- Fewer invented answers, because the model is grounded in sources.
- Access control — retrieve only what a user is allowed to see.

## RAG vs fine-tuning

|   | RAG | Fine-tuning |
| --- | --- | --- |
| Best for | Facts that change, large document sets | Style, format, narrow skills |
| Updating | Edit the documents | Retrain the model |
| Traceability | Can cite sources | Hard to trace |

## Frequently asked questions

### Does RAG stop AI hallucinations?

It reduces them a lot when retrieval is good, but answers should still be tested and the model told to say when the sources do not cover a question.

### Can RAG use data from my CRM or database?

Yes. Retrieval can query structured systems as well as documents, with the same access rules.