Building with LLMs and AI tooling from a .NET engineer’s perspective: SDKs, embeddings, function calling, and what actually ships.
Ollama C# tutorial: run Llama and other open models locally and call them from .NET via OllamaSharp or the OpenAI-compatible API — private and free.
LLM structured output in C#: use OpenAI structured outputs and JSON schema to get typed, deserializable results — no more regex-parsing model prose.
Function calling with OpenAI in C#: how tool calls work under the hood, the invocation loop, and the security rules for exposing .NET methods to an LLM.
Vector embeddings in .NET explained: what embedding vectors are, cosine similarity in C#, semantic search, and when you need a real vector database.
RAG C# implementation guide: chunking, embeddings, vector search and grounded prompts — build a document Q&A system in .NET with real code.
Semantic Kernel C# tutorial: build a chatbot with conversation memory, plugins, and streaming in ASP.NET Core — a complete working example for .NET devs.
Microsoft.Extensions.AI tutorial: use IChatClient to swap OpenAI, Azure and Ollama models freely, add middleware, and future-proof your .NET AI code.