What Is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is the gold standard architecture for building AI systems that are both intelligent and factually accurate. Instead of relying solely on a Large Language Model's pre-trained knowledge (which can hallucinate), RAG dynamically retrieves relevant information from your company's documents, databases, and knowledge bases at query time, then uses the LLM to synthesize a precise, source-cited answer.
This approach eliminates AI hallucinations, ensures responses are always grounded in your latest data, and enables your organization to deploy trustworthy AI assistants, chatbots, and search systems that employees and customers can rely on.














