Implement advanced RAG pipelines with Azure AI Search and Microsoft Foundry

  • Module
  • 7 Units
Advanced
AI Engineer
Solution Architect
Data Scientist
Azure
Microsoft Foundry
Azure AI Search

Implement advanced RAG pipelines for production AI agents using Azure AI Search and Microsoft Foundry. Design hybrid search architectures combining BM25 keyword and semantic vector retrieval, implement reranking strategies for high-precision knowledge access, configure dynamic knowledge source routing across multiple indexes, and optimize chunking and embedding strategies.

Learning objectives

By the end of this module, you're able to:

  • Design hybrid search architectures combining keyword and semantic retrieval for high-precision knowledge access
  • Implement reranking strategies to improve RAG retrieval quality and contextual relevance
  • Configure dynamic knowledge source routing to direct queries intelligently across multiple knowledge bases
  • Optimize chunking and embedding strategies for different content types and retrieval requirements

Prerequisites

Before starting this module, you should have:

  • Familiarity with basic RAG using Foundry IQ or Azure AI Search
  • Experience configuring data sources and retrieval in Microsoft Foundry
  • Understanding of vector embeddings and semantic search concepts
  • Proficiency in Python

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