COMPEL Glossary / rag-retrieval-augmented-generation
RAG (Retrieval-Augmented Generation)
Retrieval-Augmented Generation (RAG) is an AI architecture pattern that enhances the accuracy and reliability of large language model outputs by first retrieving relevant information from external knowledge sources (databases, documents, knowledge bases) and then including that retrieved information in the context provided to the model for response generation.
What this means in practice
RAG addresses the fundamental limitation that language models can only generate text based on their training data, which may be outdated, incomplete, or incorrect for specialized domains. For organizations deploying generative AI for enterprise use cases, RAG is often essential for producing accurate, domain-specific, and up-to-date responses. In COMPEL, RAG architectures are assessed within the Technology pillar and designed as part of the AI platform strategy in Module 3.3.
Related Terms
Other glossary terms mentioned in this entry's definition and context.