Retrieval-Augmented Generation (RAG) is an innovative method that combines retrieval capabilities with generative models to produce more accurate and relevant responses. By leveraging external data sources, RAG overcomes the common limitations of Large Language Models (LLMs), such as outdated information and the risk of hallucinations (misinformation). Additionally, RAG offers a significant advantage: it can be […]
