Retrieval-Augmented Generation (RAG) System

RAG combines retrieval and generation to create more accurate AI responses. 

The process works in three steps:
1. Document Ingestion: Documents are split into chunks and converted to vector embeddings
2. Retrieval: When a query comes in, relevant chunks are found using similarity search
3. Generation: The LLM uses retrieved context to generate accurate, grounded answers

Benefits of RAG:
- Reduces hallucinations by grounding responses in actual documents
- Enables knowledge updates without retraining models
- Provides source citations for transparency
- Works with private, domain-specific data

RAG is ideal for enterprise knowledge bases, customer support, and research applications.