RAG on the Microsoft Stack

Microsoft is enabling Retrieval Augmented Generation (RAG) on the Microsoft Azure stack by integrating with Azure OpenAI Service for large language models, supporting Faiss and Azure AI Search as vector stores, and utilizing tools like LangChain for data chunking.

Azure Machine Learning offers various features to support RAG-based scenarios, including samples for starting Q&A scenarios, a wizard-based UI experience, and the ability to measure and enhance RAG workflows.

In essence, Microsoft's approach involves leveraging RAG to connect real-time data to generative AI applications, improving accuracy by providing context to LLMs at inference time. This is achieved through indexing data efficiently using an Index asset in Azure AI Search, which supports information retrieval over stored textual and vector data.