Retrieval Augmented Generation (RAG) systems are revolutionizing AI by enhancing pre-trained language models (LLMs) with external knowledge. Leveraging vector databases, organizations are crafting RAG systems tailored to internal data sources, amplifying LLM capabilities. This fusion is reshaping how AI interprets user queries, delivering contextually relevant responses across domains.
As the name suggests, RAG augments the pre-trained knowledge of LLMs with enterprise or external knowledge to generate context-aware domain specific responses. To derive higher business value from large language foundation models, many organizations are leveraging vector databases for building RAG systems with enterprise internal data sources.
