Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is the dominant architecture for building enterprise AI applications that require high accuracy and factual precision. A standard Large Language Model (LLM) generates text based solely on the data it was trained on months or years ago. It has no access to your company's live database, private emails, or secure HR policies. RAG bridges this gap by marrying the reasoning power of an LLM with the real-time search capabilities of your internal systems.

When a user asks a RAG-enabled system a question, the system first executes a search (usually a 'vector search') against a secure corporate database to retrieve relevant documents. It then injects those documents directly into the prompt and instructs the LLM to generate an answer based explicitly on that retrieved text. This dramatically reduces hallucinations, allows the AI to cite its sources, and ensures the information is up-to-date.

However, building RAG is easy; governing RAG is exceptionally difficult. If the search component retrieves a highly confidential financial document and feeds it to the LLM, the AI will happily summarize that secret data for an unauthorized user. Enterprise RAG deployments require rigorous governance—like Remova's 'Intentional Grounding'—to ensure the retrieval engine strictly respects Role-Based Access Control (RBAC) and only feeds the LLM data the user is explicitly authorized to see.