Retrieval-Augmented Generation
An AI framework that improves model responses by dynamically fetching verified data from external, proprietary databases.
Retrieval-Augmented Generation (RAG) is a breakthrough AI architectural framework that significantly enhances 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 highly relevant, proprietary documents and feeds them to the LLM as context.
This ensures that the AI's answer is grounded in verified, up-to-date enterprise data, virtually eliminating AI "hallucinations" while simultaneously respecting internal data governance and access controls.