Implement AI capabilities in database solutions

  • Learning Path
  • 3 Modules

At a glance

This learning path explores how to implement AI capabilities directly in Azure SQL Database. You learn to design intelligent search using full-text and vector search, integrate AI models and embeddings, and build Retrieval Augmented Generation (RAG) solutions entirely in T-SQL.

Prerequisites

Before starting this learning path, you should have experience working with Azure SQL Database or SQL Server, writing Transact-SQL queries, and a general understanding of AI concepts.

Modules in this learning path

Design and implement models and embeddings with SQL

Integrate AI models with Azure SQL Database using external models and built-in AI functions. Design effective embedding strategies and implement maintenance patterns to keep embeddings aligned with source data.

Design and implement intelligent search with SQL

Implement intelligent search capabilities in SQL Server and Azure SQL by combining traditional full-text search with semantic vector search. Establish a mental model for different search approaches, prepare SQL for vector-based search, and implement vector, hybrid, and ranking-based search patterns with performance considerations.

Design and implement RAG with SQL

This module teaches you how to implement Retrieval Augmented Generation (RAG) using Azure SQL Database. You learn to identify appropriate RAG scenarios, prepare SQL results as LLM context, construct augmented prompts, and process model responses.

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