Enhanced Enterprise AI Chat Applications with Custom Business Data

This article demonstrates how Microsoft Fabric is used to orchestrate and implement Retrieval Augmented Generation (RAG) for AI-powered chat and query applications. It outlines in detail how to build a solution that delivers precise, contextually relevant business responses by integrating Azure OpenAI for embedding and chat, Azure AI Search for vector indexing and retrieval, and orchestrating the process inside Microsoft Fabric. The guide uses a public Wikipedia QA dataset to demonstrate chunking, embedding, indexing, and retrieval procedures inside a unified pipeline. Extensive technical instructions are included, covering data chunking, Spark integration, vector index creation, and retrieval-augmented question answering. The RAG system ensures dynamic updates and precise results, especially for businesses needing specialized knowledge not available in general purpose LLMs. Notebooks and widgets for demonstration are provided, encouraging application to business datasets.