Retrieval Augmented Generation (RAG)
An AI architecture that retrieves relevant documents before generating responses, combining search with generation.
Definition
RAG systems first search knowledge bases for relevant information, then use retrieved content to inform AI responses. This approach enables accurate answers about specific documents or domains.
The architecture separates knowledge storage from reasoning, allowing updates without model retraining. Organizations can deploy RAG with proprietary information while using standard AI models.
Why It Matters
RAG enables enterprise AI applications by connecting models to organizational knowledge. It solves the problem of making AI knowledgeable about company-specific information.
For AI deployment, RAG offers faster, cheaper customization than fine-tuning or training custom models.
Examples in Practice
A law firm's RAG system retrieves relevant case law before answering legal questions. A customer service AI retrieves product documentation before responding to support queries.
RAG implementations require careful attention to retrieval quality—poor search results lead to poor AI responses.