What is RAG (Retrieval-Augmented Generation)?

Retrieval-Augmented Generation (RAG) is an AI architecture that enhances LLM responses by querying external vector databases for relevant document chunks before generating a response.

Detailed Explanation

RAG solves LLM data freshness and hallucination limitations. Instead of relying on static training weights, the RAG engine converts internal documents into vector embeddings. When a user asks a query, the system retrieves exact document passages and feeds them to the LLM prompt context as ground truth.

Enterprise Examples

• • Enterprise internal policy search over thousands of PDF manuals
• • Clinical EHR data retrieval for medical treatment guidance