Introduction

Building an AI feature is easy if all you want is a textbox that sends a prompt to an LLM and prints the response.

Building an AI application that users can actually depend on is a different problem.

The moment an AI feature becomes part of a real application, you have to deal with authentication, conversation history, application data, failed model calls, prompt injection, rate limits, logging, testing, cost, and eventually scaling.

The LLM becomes one component inside your application rather than the application itself.

That is where ASP.NET Core fits well. It already gives .NET developers the HTTP pipeline, dependency injection, configuration, authentication, authorization, logging, middleware, testing ecosystem, and deployment options needed around an AI service.

In this article, we'll build a realistic AI Knowledge Assistant with ASP.NET Core and C#.…