# RAG (Retrieval-Augmented Generation) Overview

## What is RAG?

RAG combines retrieval of relevant information with text generation. It allows LLMs to access and reference external knowledge sources.

## Components

1. **Retriever** - Finds relevant documents from a knowledge base
2. **Generator** - LLM that generates answers using retrieved context
3. **Vector Store** - Stores document embeddings for similarity search

## How it works

1. User query is converted to embeddings
2. Similar documents are retrieved from vector store
3. Retrieved context is combined with query
4. LLM generates answer based on context

## Benefits

- Access to up-to-date information
- Reduced hallucinations
- Source attribution
- Cost-effective compared to fine-tuning

## Common Use Cases

- Question answering over documents
- Chat with your data
- Research assistance
- Knowledge management