Retrieval-Augmented Generation (RAG) represents a significant advancement in leveraging generative AI with business-specific data. This AI architecture allows organizations to utilize information from their databases, documents, and live data streams, enhancing the precision and relevancy of AI-generated responses. The maturity of RAG technology has led to widespread adoption among businesses, who benefit from tailored outputs. However, research indicates potential risks, including an increased likelihood of generating unsafe or misleading responses, necessitating careful implementation and oversight in business applications.
RAG enables large language models (LLMs) to access and reason over external knowledge stored in databases, documents, and live in-house data streams.
Maxime Vermeir describes RAG as a system that enables you to generate responses not just from its training data but also from the specific, up-to-date knowledge you provide.
Hundreds, perhaps thousands, of companies are already using RAG AI services, with adoption accelerating as the technology matures.
According to Bloomberg Research, RAG can vastly increase the chances of getting dangerous answers.
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