Learn Azure AI Search with C# — index data, run vector and hybrid queries, and add RAG to your .NET app. Start building intelligent search today. If your application's search box still runs a LIKE '%term%' query against SQL Server, your users are quietly suffering. They type "cheap laptop for uni" and get zero results because your catalogue says "affordable notebook for students." Azure AI Search with C# fixes exactly this problem: it combines classic keyword search, vector embeddings, and semantic reranking into a single managed service that you can drive from .NET with a few dozen lines of code. In this tutorial you'll build a working search index from scratch, run keyword, vector, and hybrid queries, and finish with a Retrieval Augmented Generation (RAG) pattern that grounds an LLM in your own data. This guide targets .NET 9 and the Azure.Search.Documents v11 SDK. Every snippet is runnable. We'll explain why each design choice matter...
Learn Entity Framework Core 9 migrations, relationships, and performance tips with runnable C# examples. Master EF Core 9 best practices today. Entity Framework Core is the default data-access layer for modern .NET applications, and Entity Framework Core 9 (shipped with .NET 9) is the most capable release yet. Whether you're a beginner learning how to create your first migration, an intermediate developer looking for EF Core best practices, or a senior engineer hunting down slow queries, this Entity Framework Core 9 tutorial covers the three areas that matter most in real projects: migrations , relationships , and performance . Every example is runnable C# you can drop into an ASP.NET Core or console app. What's New in Entity Framework Core 9? Before diving in, here's why EF Core 9 is worth upgrading to: Improved LINQ translation — more queries translate to SQL instead of throwing "could not be translated" exceptions, including better support for GroupB...