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Beginner ~8 hours · 12 articles

AI Foundations for .NET Developers — Generative AI, LLMs, and Prompt Engineering in C#

Build a solid understanding of AI concepts — from how LLMs work to RAG, prompt engineering, and generative AI — all through the lens of C# and .NET.

What You'll Learn

  • Understand how large language models work and their capabilities
  • Apply prompt engineering techniques in C# applications
  • Explain RAG architecture and when to use it
  • Compare LLM providers and their .NET SDKs
  • Evaluate AI integration opportunities in existing .NET projects

Prerequisites

  • C# fundamentals
  • .NET 8+ development environment

Learning Path Articles

Your Progress 0 of 12 completed
Start: Your First AI App in .NET: Make One API Call and See What Happens
  1. 1

    Your First AI App in .NET: Make One API Call and See What Happens

    Build your first Azure AI Foundry app in C# in 15 minutes. No Python. No complexity. One API call with GPT-5.4-mini and .NET 10 that changes how you think about AI.

    Beginner 10 min read workshop
  2. 2

    Microsoft.Extensions.AI: IChatClient Guide

    Learn Microsoft.Extensions.AI in .NET: IChatClient, IEmbeddingGenerator, provider-agnostic chat, middleware, caching, logging, and DI.

    Intermediate 12 min read university
  3. 3

    Generative AI for .NET Developers

    Learn generative AI for .NET: LLMs, tokens, embeddings, Semantic Kernel, Microsoft.Extensions.AI, ML.NET, and ONNX in C#.

    Beginner 10 min read university
  4. 4

    How LLMs Work: Tokens, Context Windows & Next-Token Prediction Explained

    How GPT, Claude, and LLMs generate text via next-token prediction. Tokens, context windows, temperature, and transformers — explained for .NET developers.

    Beginner 12 min read university
  5. 5

    MEAI vs Semantic Kernel vs Agent Framework

    Choose the right .NET AI library: Microsoft.Extensions.AI for abstraction, Semantic Kernel for orchestration, Agent Framework for agents.

    Intermediate 12 min read university
  6. 6

    Prompt Engineering in C#: System Messages, Few-Shot & Structured Output

    Prompt engineering with C# code examples: system messages, few-shot prompting, structured JSON output, and common mistakes. Azure OpenAI + Semantic Kernel.

    Beginner 14 min read university
  7. 7

    DI for AI Services in ASP.NET Core: IChatClient, Kernel & Keyed Services

    Register IChatClient, IEmbeddingGenerator, and Semantic Kernel in ASP.NET Core DI. Keyed services for multi-model, plugin injection, and unit testing patterns.

    Intermediate 13 min read university
  8. 8

    LLM Providers for .NET: OpenAI vs Azure

    Compare OpenAI, Azure OpenAI, Anthropic, and Gemini for .NET apps: SDK support, pricing, rate limits, and Semantic Kernel connectors.

    Beginner 12 min read university
  9. 9

    Azure OpenAI Token Counting in C#

    Count Azure OpenAI tokens in C# with Microsoft.ML.Tokenizers. Add budgets, prevent context overflow, estimate cost, and enforce limits.

    Intermediate 13 min read university
  10. 10

    Azure OpenAI Structured Outputs: C# JSON Fix

    Get valid JSON from Azure OpenAI in C#. Use ChatResponseFormat, JSON schema, MEAI, and Semantic Kernel patterns that avoid 400 errors.

    Intermediate 13 min read university
  11. 11

    Vector Databases for .NET Compared

    Compare Cosmos DB, Azure AI Search, Qdrant, pgvector, and SQL Server for .NET RAG apps. Includes C# and Semantic Kernel guidance.

    Intermediate 16 min read university
  12. 12

    Phi-4 Local in C#: Ollama vs ONNX vs Foundry

    Run Phi-4 locally in .NET and compare Ollama, ONNX Runtime GenAI, and Foundry Local with setup code and Semantic Kernel examples.

    Intermediate 14 min read university