Retrieval-Augmented Generation (RAG) Enhances AI Accuracy and Reliability by Connecting to Trusted Data Sources
Retrieval-Augmented Generation (RAG) is a technology designed to improve the accuracy and reliability of Artificial Intelligence (AI) systems by linking them to trusted external data sources. Unlike traditional AI models that rely solely on their pre-trained knowledge, RAG enables AI to retrieve real-time, factual information. This process helps AI models to generate responses that are more accurate, reliable, and grounded in verifiable facts, thereby reducing instances of 'hallucinations' or fabricated information. By connecting AI to a dynamic and reliable data repository, RAG ensures that the AI's outputs are consistently supported by evidence, making the technology more dependable for various applications.
