University

University

Master .NET + AI fundamentals. Structured learning paths from beginner to intermediate — architecture patterns, SDK deep-dives, and conceptual guides that build real understanding.

The University is where your .NET + AI journey starts. Each learning path is a structured sequence of articles — designed to take you from foundational concepts to production-ready knowledge. Pick the path that matches where you are, and work through it in order.

AI Foundations for .NET Developers

Beginner

Start here. Understand generative AI, LLMs, prompt engineering, and the .NET AI ecosystem from the ground up.

  1. 1

    What Is Generative AI and Why Should .NET Developers Care

    Understand what generative AI, LLMs, and transformers actually are — explained for C# developers who want to build AI systems without switching to Python.

    10 min read .NET 9
  2. 2

    How Large Language Models Work — A Mental Model for Engineers

    Understand how GPT, Claude, and other LLMs actually generate text — training, inference, temperature, and token prediction explained for engineers.

    12 min read .NET 9
  3. 3

    Prompt Engineering Fundamentals for C# Developers

    Learn prompt engineering best practices with C# examples — system messages, few-shot prompting, structured output, and common mistakes to avoid.

    14 min read .NET 9
  4. 4

    Comparing LLM Providers — OpenAI, Azure OpenAI, Anthropic, and Open-Source

    Compare OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, and open-source models for .NET applications — pricing, features, and SDK support.

    12 min read .NET 9

.NET AI Development with Semantic Kernel

Intermediate

Deep-dive into Semantic Kernel architecture, function calling, RAG pipelines, and multi-agent systems.

  1. 1

    Semantic Kernel Architecture Deep Dive

    Understand the internal architecture of Microsoft Semantic Kernel — plugins, planners, memory, and the kernel pipeline — so you can build reliable AI agents with .NET.

    12 min read .NET 8
  2. 2

    Function Calling and Tool Use with Semantic Kernel

    Implement function calling and tool use with Semantic Kernel — register C# plugins, enable auto-invocation, and let the AI orchestrate your code.

    15 min read .NET 9
  3. 3

    Retrieval-Augmented Generation Explained for .NET Architects

    Understand RAG architecture — embeddings, vector databases, retrieval pipelines, and grounded generation — designed for .NET architects building AI systems.

    14 min read .NET 9
  4. 4

    Microsoft Agent Framework and Multi-Agent Patterns in .NET

    Learn Microsoft's Agent Framework for .NET — agent definitions, workflows, tool registration, and multi-agent orchestration patterns built on Semantic Kernel.

    14 min read .NET 9

Production AI Engineering

Intermediate

Security, orchestration patterns, and the engineering practices needed to ship AI systems to production.

  1. 1

    Securing AI Applications in .NET — API Keys, Data Privacy, and Guardrails

    Production security guide for .NET AI applications — secret management, PII handling, content filtering, prompt injection defense, and observability.

    12 min read .NET 9
  2. 2

    Designing AI Workflows — Orchestration Patterns for .NET Applications

    Design reliable AI workflows in .NET — prompt chaining, sequential pipelines, parallel fan-out, human-in-the-loop, and agent orchestration patterns.

    13 min read .NET 9

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