Retrieval Augmented Generation (RAG) is an advanced AI framework that enhances large language models (LLMs) by integrating an external information retrieval system. Unlike traditional LLMs, which are limited to the knowledge contained in their training data, RAG allows these models to access and retrieve up-to-date information from trusted external sources, a process known as grounding. This improves the relevance and factual accuracy of responses.
For professional and higher education applications, RAG might draw from proprietary data sources, library subscription resources, program-specific accreditation standards, competency-based education frameworks, other relevant ontologies, and proprietary domain-specific technologies. By retrieving and integrating relevant data from these sources, RAG ensures responses are both contextually rich and aligned with domain-specific knowledge.
RAG offers institutions the ability to supplement and enhance LLMs built with proprietary data to achieve specific goals. Because RAG inputs can be restricted to verified documents, policies, and frameworks, institutions can maintain a high degree of control over the accuracy and relevance of the information provided. Professional and higher education institutions, including medical, nursing, dental, pharmacy, and veterinary schools, can leverage RAG to manage complex data structures, improve curricular outcomes, and streamline processes such as accreditation preparation.