End-to-End RAG Workflow: How Retrieval Augmented Generation Works

Retrieval Augmented Generation (RAG) is a technique that combines information retrieval with generative AI to enhance model outputs by fetching relevant external documents or data before generating responses. The workflow typically involves indexing a knowledge base, retrieving relevant documents based on user queries, and feeding those retrieved documents to a language model to generate more accurate and contextual answers. This approach helps reduce hallucinations and enables AI systems to work with up-to-date or domain-specific information beyond their training data.

Source: Databricks | 2026-06-23