What is RAG? Beginner Guide

Retrieval Augmented Generation (RAG) enhances Large Language Model (LLM) responses by retrieving relevant information from external data sources before generating answers. This approach improves accuracy and reduces hallucinations by grounding responses in factual data.

How RAG Works

RAG combines a retriever which fetches relevant documents from a database with a generator that formulates answers. Vector databases and embeddings power the retrieval process by converting text into numerical vectors that capture semantic meaning.

Why RAG Matters

RAG provides reliable answers for specialized domains like healthcare or legal services where accuracy is critical. It allows enterprises to leverage their proprietary data without retraining models, reducing costs.

Key Use Cases

• Customer support chatbots with access to knowledge bases
• Research assistants that cite sources directly
• Enterprise search tools for internal documents