This Microsoft patent introduces an advanced two-stage Retrieval-Augmented Generation (RAG) system designed to improve how AI chatbots answer questions. By moving away from traditional keyword-based searches, the framework uses a structured knowledge graph to minimize irrelevant data and maximize factual precision. The process utilizes two distinct large language models: one to translate conversational intent into a technical graph query and another to generate a natural, grounded response. This architecture relies on entity normalization and attribute extraction to ensure that specific details, such as price or location, are accurately retrieved. For digital content creators, this shift highlights the importance of providing explicit, structured data rather than just descriptive prose. Ultimately, the system aims to resolve ambiguity and provide context-aware answers that remain highly relevant throughout a back-and-forth conversation.
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