Intel has introduced a detailed explanation of retrieval augmented generation (RAG), a method that enhances AI model performance by incorporating external data sources. According to Intel, RAG combines the strengths of retrieval systems and generative models to provide more accurate and contextually relevant responses. This approach allows AI systems to access up-to-date information, which is particularly useful in dynamic environments where data is constantly changing. The company emphasized that RAG can be applied across various industries, including healthcare, finance, and customer service, to improve decision-making processes. Intel also highlighted that the integration of external data helps reduce the risk of outdated or incorrect information being used in AI outputs. The method is designed to work with existing models, making it a flexible solution for organizations looking to enhance their AI capabilities. Intel stated that RAG is a key component in the evolution of AI systems, enabling them to deliver more reliable and informed results. *Source: [intel](https://medium.com/intel-tech/understanding-retrieval-augmented-generation-rag-4d1d08f736b3?source=rss----bcaa5b033cbb---4)*