You can go from an empty browser tab to a running C# program that talks to a large language model without installing a single thing, without an API key, and without a credit card. Open Microsoft's Generative AI for Beginners .NET in GitHub Codespaces, let the container build, and the first sample calls a model through GitHub Models on the free tier. That first friction-free run is the whole pedagogical bet of the course: get the model call working in two minutes, then spend the rest of the time on the parts that actually matter.
I went through the course to figure out what a working .NET developer should take from it. The short version: the syllabus is fine, but the real payload is a single abstraction — IChatClient from Microsoft.Extensions.AI — and once that clicks, most of the "AI" mystique collapses into ordinary dependency-injection code you already write every day.
What the course actually is
It's a free, open-source, five-lesson course (MIT licensed) aimed at .NET developers who already know the basics and want to add generative AI to real applications. The repository was rewritten from scratch for Version 2, which targets .NET 10 and reorganizes everything into five lessons:
- Introduction to Generative AI — what generative AI is, why .NET matters here, the Microsoft AI stack, and getting a sample running.
- Generative AI Techniques — chat with memory, text embeddings, processing images and documents, and calling models through abstractions.
- AI Patterns and Applications — semantic search, retrieval-augmented generation (RAG), and document processing.
- AI Agents with Microsoft Agent Framework — tool calling, multi-agent orchestration, and Model Context Protocol (MCP) integration.
- Responsible AI — bias, content-safety guardrails, transparency, and the ethics of agentic systems.
Each lesson pairs a short 5–10 minute video with fully runnable code samples. It's a "read the code, run the code, change the code" course, not a lecture series. That format is the right call for this audience.
The abstraction worth learning
Version 1 leaned on Semantic Kernel as the foundation. Version 2 swaps that out for Microsoft.Extensions.AI (MEAI) as the primary abstraction, and the reasoning is the important part: MEAI ships as part of the .NET ecosystem and follows the same patterns as ILogger and IConfiguration. You program against an interface; the concrete provider is a registration detail.
Concretely, every model provider — GitHub Models, Azure OpenAI, a local model via Ollama or Foundry Local — hides behind the same IChatClient:
using Microsoft.Extensions.AI;
// Swap this one line to change providers. Everything downstream is identical.
IChatClient client = new OllamaChatClient(
new Uri("http://localhost:11434/"), "llama3.2");
ChatResponse response = await client.GetResponseAsync(
"Explain dependency injection in one sentence.");
Console.WriteLine(response.Text);
Your service layer takes IChatClient in its constructor and never learns which model answered. Develop against a local Ollama model for free, register Azure OpenAI in production, and the code between those two worlds does not change. That is the single most useful idea in the whole course, and it's why the "beginners" framing undersells it — plenty of teams shipping AI today don't have this seam and are paying for it in coupling.
It gets more interesting because MEAI is a composable pipeline, the same builder shape you'd recognize from middleware:
IChatClient client = baseClient
.AsBuilder()
.UseFunctionInvocation()
.UseOpenTelemetry()
.UseDistributedCache(cache)
.Build();
Function calling, telemetry, and caching become decorators you opt into, not bespoke plumbing you hand-roll per project. If you've built ASP.NET Core middleware, you already understand this mental model.
Where the course earns its time
Lessons 2 and 3 are the core. The RAG material is the most transferable — semantic search over embeddings plus a grounded prompt is the pattern behind most "chat with your data" features shipping right now, and seeing it built with native SDKs rather than a heavyweight framework demystifies it. If your goal is a product feature this quarter, start here and skim the rest.
Lesson 4 is the one that has changed the most. It uses the Microsoft Agent Framework for tool use and multi-agent orchestration, and it wires in MCP so your agent can call external tools through a standard protocol instead of hand-written glue. This is the fastest-moving corner of the .NET AI story, so treat the samples as a current snapshot rather than settled doctrine — but the shape of it (an agent as an IChatClient with tools attached) is stable.
Lesson 5 puts Responsible AI at the end, and I'd argue that's the one placement I'd change. Content-safety guardrails and grounding checks are cheaper to design in than to retrofit. Read it first, then build.
Who should skip parts of it
If you've already shipped an LLM feature, Lesson 1 is orientation you don't need — jump straight to the MEAI abstraction samples. If you're allergic to Azure, note that the course leans on GitHub Models and Codespaces for zero-setup, but the local path (Ollama, Foundry Local) is real and fully supported, so you can do the entire thing without a cloud bill. And if you were hoping for deep prompt-engineering theory, this isn't that course; it's deliberately about wiring, patterns, and running code.
The takeaway
Don't watch this course. Fork it, open it in Codespaces, and run Lesson 2's chat sample against GitHub Models. Then change exactly one line — the client registration — to point at a local Ollama model, and confirm your app behaves identically. When that swap works and nothing else in your code moves, you've internalized the actual lesson: in modern .NET, a model provider is a dependency like any other, and building generative AI apps is mostly the disciplined DI and pipeline work you already know how to do well.
Sources: Generative AI for Beginners .NET (GitHub) · Generative AI for Beginners .NET: Version 2 on .NET 10 (.NET Blog) · Announcing Generative AI for Beginners – .NET (.NET Blog)