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 how to fine-tune AI models and call them from C# with OpenAI and Azure OpenAI. Step-by-step code examples inside — start building today. Why Fine-Tuning AI Models Matters for C# Developers Fine-tuning AI models is one of the most searched-for skills in modern software development — and for good reason. While base models like GPT-4o and Claude are impressively general, they don't know your company's tone of voice, your domain-specific terminology, or your internal classification rules. Fine-tuning solves this: you take a pre-trained model and continue training it on your own examples, producing a custom model that responds exactly the way your business needs. In this guide, you'll learn how fine-tuning works, when to use it (and when not to), how to prepare training data, how to run a fine-tuning job, and — most importantly for us — how to call your fine-tuned model from a C# application using .NET 8/9. Here's the WHY before the HOW: a fine-tuned smaller mode...