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

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

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, IEmbeddingGenerator & Middleware in .NET

   Write provider-agnostic .NET AI code with Microsoft.Extensions.AI: IChatClient for chat, IEmbeddingGenerator for vectors, and middleware pipelines for caching/logging.

   Intermediate 12 min read university
3. 3

   Generative AI for .NET Devs: LLMs, Tokens & the C# AI Stack Explained

   LLMs, transformers, and tokens explained for C# developers. Map the full .NET AI stack — Semantic Kernel, Microsoft.Extensions.AI, ML.NET, ONNX Runtime.

   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: Which .NET AI Library in 2026?

   Pick the right Microsoft AI library for .NET: MEAI for abstraction, Semantic Kernel for orchestration, Agent Framework for autonomy. Stack diagram + decision guide.

   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 Compared: OpenAI vs Azure vs Anthropic vs Gemini

   Compare OpenAI, Azure OpenAI, Anthropic Claude, and Google Gemini for .NET: pricing, rate limits, SDK support, and Semantic Kernel connector availability.

   Beginner 12 min read university
9. 9

   Token Counting in C# for Azure OpenAI: Budget, Track & Prevent Overflow

   Count tokens in C# with Microsoft.ML.Tokenizers, implement per-request budgets, and prevent context overflow. Cost estimation patterns + middleware example.

   Intermediate 13 min read university
10. 10

    Azure OpenAI Structured Outputs in C#: Guaranteed JSON Schema Compliance

    Get guaranteed JSON from Azure OpenAI in C#: SDK ChatResponseFormat, MEAI, and Semantic Kernel approaches. Schema design patterns + edge case handling.

    Intermediate 13 min read university
11. 11

    Vector Databases for .NET Compared: Cosmos DB, AI Search, Qdrant & pgvector

    Compare vector databases for C#: Cosmos DB, Azure AI Search, Qdrant, pgvector, and SQL Server 2025. Semantic Kernel connector ratings + code examples.

    Intermediate 16 min read university
12. 12

    Run Phi-4 Locally in C#: Ollama vs ONNX vs Foundry Local (Benchmarked)

    Run Phi-4 locally in .NET: compare Ollama, ONNX Runtime GenAI, and Foundry Local. Setup code, Semantic Kernel integration, and performance benchmarks.

    Intermediate 14 min read university