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+# 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