RAG (Retrieval-Augmented Generation) models have been widely applied across various domains, primarily including:

Applications in Intelligent Question-Answering Systems

RAG generates accurate and detailed answers by retrieving real-time external knowledge bases, avoiding the misinformation that traditional generative models might produce. For example, in medical Q&A systems, RAG can integrate the latest medical literature to generate precise answers with up-to-date treatment plans, preventing outdated or incorrect recommendations. This approach helps medical experts quickly access the latest research findings and diagnostic advice, enhancing the quality of medical decision-making.

Case Study: Medical Q&A System

  • User Query via Web Application
  • a. A user submits a query through a web application. The request enters the backend system, initiating the data processing workflow.
  • Authentication via Azure AD
  • a. The system authenticates the user through Azure Active Directory (Azure AD), ensuring only authorized users can access the system and data.
  • User Permission Check
  • a. The system filters accessible content based on the user’s group permissions (managed by Azure AD), ensuring users only see information they are permitted to view.
  • Azure AI Search Service
  • a. The filtered user query is passed to the Azure AI Search service, which searches indexed databases or documents for relevant content. This engine retrieves the most pertinent information using semantic search technology.
  • Document Intelligence Processing
  • a. The system processes input documents using OCR (Optical Character Recognition) and document extraction technologies, converting unstructured data into structured, searchable data for efficient retrieval by Azure AI.
  • Document Sources
  • a. These documents originate from pre-stored collections that have been prepared and indexed via document intelligence processing before being queried by users.
  • Response Generation via Azure OpenAI
  • a. After retrieving relevant information, the data is passed to Azure OpenAI. This module leverages Natural Language Generation (NLG) to produce coherent responses based on the user’s query and retrieved results.
  • Response Returned to User
  • a. The final generated answer is returned to the user through the web application, completing the end-to-end query-to-response workflow.

Summary

This workflow demonstrates the integration of Azure AI technologies—document retrieval, intelligent processing, and natural language generation—to handle complex queries while ensuring data security and compliance.