AI Engineering with .NET: A Practical Guide If you write C# for a living, there is a good chance someone has already asked you to "add AI" to an application. Maybe the requirement is document summarization, answering questions over company policies, classifying support tickets, generating content, extracting structured data, or building a chatbot. The interesting part is that most of these problems do not require you to become a machine-learning researcher. They require you to understand how AI models fit into a software system: how to call them, structure their output, provide application context, handle failures, control cost, evaluate responses, and keep the system observable in production. That is where AI engineering with .NET becomes useful. If you already work with C#, ASP.NET Core, dependency injection, APIs, databases, background services, logging, and distributed systems, you already have much of the foundation requir...