Skip to main content
Implementing Retrieval-Augmented Generation (RAG) with Azure OpenAI Service Retrieval-Augmented Generation (RAG) combines the fluency of large language models with the precision of retrieval systems to generate answers grounded in your own data. In this module we’ll explain the core concepts of RAG, show how Azure OpenAI Service supports RAG workflows, and demonstrate practical approaches to integrate your structured and unstructured content into model responses.
A presentation slide titled "Learning Objectives" listing three points about Retrieval-Augmented Generation (RAG): understanding RAG with custom data, using REST APIs to implement RAG-based solutions, and leveraging language-specific SDKs to enhance RAG workflows.
This lesson focuses on three practical outcomes: By the end of this module you’ll be able to design and implement RAG solutions that augment Azure OpenAI model outputs with relevant data from your own sources—documents, knowledge bases, databases, and more.
Before you begin, make sure you have access to Azure OpenAI resources and a dataset (documents or structured data) to index. Familiarity with embeddings and vector search concepts will accelerate your progress.
What this lesson will cover, step by step:
  • Overview of RAG architectures and when to use them (hybrid vs. pure retrieval).
  • How to create embeddings for your data and store them in a vector store or search service.
  • How to retrieve relevant context and construct prompts that safely and effectively condition model outputs.
  • Implementing RAG via the Azure OpenAI REST API and leveraging language-specific SDKs to streamline the workflow.
  • Best practices for relevance, latency, hallucinatory behavior mitigation, and production deployment.
Links and references Let’s get started with the introduction.

Watch Video