An AI framework that improves model responses by dynamically fetching verified data from external, proprietary databases.
Retrieval-Augmented Generation (RAG) is an AI architectural framework that improves the accuracy and reliability of Large Language Models (LLMs).
Instead of relying solely on the static knowledge the model was originally trained on, a RAG system intercepts a user's prompt and performs a real-time search against a secure corporate vector database. It retrieves the most relevant proprietary documents and feeds them to the LLM as context.
This grounds the AI's answer in verified, up-to-date enterprise data, reducing hallucinations while respecting internal data governance and access controls.
See also Vector Database, Data Governance, Data Mobilization and Data Lakehouse.