Retrieval-Augmented Generation, or RAG, is one of the most practical architectures for making Generative AI more accurate, useful, and trustworthy. Instead of relying only on a model’s internal knowledge, RAG retrieves relevant information from trusted documents, databases, websites, or knowledge bases before generating an answer. RAG for GenAI: How Retrieval-Augmented Generation Is Powering the Future of AI RAG connects Generative AI with trusted external knowledge sources before generating answers. Introduction Generative AI has changed how people write, code, search, summarize, analyze documents, and build intelligent applications. Large language models (LLMs) can produce impressive answers, but they also have important limitations. They may generate unsupported information, use outdated knowledge, miss private company context, or fail to cite where their answers came from. This is where Retrieval-Augmented Generation , commo...
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