Building an AI Knowledge Assistant with ASP.NET Core
This tutorial shows how to grow a single LLM call into a production-ready AI feature on ASP.NET Core. It covers authentication, retrieval-augmented generation, security and failure handling. It is a practical roadmap for .NET developers who want the model to stay a replaceable dependency inside their application.
Key points
- Keep AI logic in an application service behind an interface, not in controllers.
- Register IChatClient through dependency injection once instead of creating clients per request.
- Never store AI credentials in appsettings.json; use user-secrets locally and managed identity in Azure.
- Apply authorization before retrieval so the model never sees content the user cannot access.
- Retry only transient failures such as timeouts and rate limits; do not retry authentication errors.
- Start RAG with simple search and add a vector database only when the document set demands it.
A prompt box that calls an LLM is easy to build; an AI feature that users can rely on is not. This article builds SupportDesk AI, a support-team knowledge assistant on ASP.NET Core, and grows a single model call into a layered design. The key idea is that the application keeps ownership of authentication, authorization and data access, while the model only generates language. Along the way it covers the IChatClient abstraction from Microsoft.Extensions.AI, registering the client through dependency injection, keeping secrets in user-secrets or managed identity, and storing conversation history.
The second half targets production concerns: retrieval-augmented generation over approved documents, with authorization applied before anything reaches the model, retrieved content treated as untrusted input, and tool permissions kept narrow. It also recommends timeouts, selective retries, rate limiting, careful logging of prompts, and unit tests that avoid live model calls. According to the author, a simple search may be enough before a vector database is justified.
Real-world use
- An internal support assistant that answers from product docs and past support tickets.
- A multi-tenant SaaS API adding a chat feature that must respect per-user document permissions.
- A team prototyping a model connection and then hardening it for production use.
Why it matters
Treating the model as one replaceable dependency keeps authorization, reliability and testing in the ASP.NET Core code you already know. That way the application boundary survives even when the model provider changes.