Retrieval-Augmented Generation (RAG) is an AI architecture pattern that combines the generative capabilities of large language models with real-time information retrieval from external knowledge bases. Instead of relying solely on the model's training data, RAG systems search relevant documents, databases, or APIs to ground their responses in current, factual information.
The RAG pipeline typically involves: a user query is received, the query is used to search a knowledge base (using vector similarity or keyword search), relevant document chunks are retrieved and ranked, the retrieved context is combined with the original query into an enriched prompt, and the language model generates a response grounded in the retrieved information.
Enterprise applications of RAG include: internal knowledge assistants (querying company wikis, policies, and documentation), customer support bots grounded in product documentation, legal research tools that cite actual case law, financial analysis systems referencing real market data, and HR assistants answering policy questions with accurate information.
Security and governance considerations for RAG include: access control on the knowledge base (ensuring users only retrieve documents they're authorized to see), data freshness and accuracy of the knowledge base, prompt injection risks through poisoned documents, information leakage across user contexts, and the need to validate that retrieved sources are authoritative.
