Modern C# and .NET: Minimal API, RAG, streaming, function calling and agent orchestration.
How **LLM evaluation** works in .NET: unit tests verify code, but ==a model's response is not deterministic== and must be *judged*, not compared. With *Microsoft.Extensions.AI.Evaluation* I build an ==xunit suite== that scores the *relevance*, *coherence* and *groundedness* of an assistant's answers, with a **local judge via Ollama**, disk-based caching and an ==HTML report== ready for CI.
==Hybrid search== in .NET: **dense** vectors and **BM25** keywords fused into *one ranking* with **Reciprocal Rank Fusion**. One Qdrant collection with *two named vectors*, local embeddings via *Ollama* and *Microsoft.Extensions.AI*, and a **Minimal API** that compares the three modes — *dense*, *keyword*, *hybrid* — over the same knowledge base. Then the level-up: a **RAG** endpoint that sends the retrieved documents to a local LLM, a **semantic cache** of the answers on Qdrant, **Redis** for output caching and **MongoDB** with a generic repository and pagination. Bonus: the AI provider becomes an *appsettings* switch — Ollama or any **OpenAI-compatible** endpoint — plus a hard look at **Microsoft Agent Framework**.
==Semantic caching== for **LLMs** in .NET: a *two-layer* cache that answers **repeated** and *reworded* questions without re-running the model. Exact match with *UseDistributedCache*, a *semantic* layer with embeddings and vector search on **Redis 8**, in a **Minimal API** with *Microsoft.Extensions.AI* and *Ollama*.
LLM responses that arrive **token by token**, just like ChatGPT: in this tutorial I build a ==.NET 10 Minimal API== that streams from a local model with *Ollama* and *Microsoft.Extensions.AI*, and exposes it to the browser with ASP.NET Core's native **Server-Sent Events** — TypedResults.ServerSentEvents, zero extra libraries.
Structured output with *Microsoft.Extensions.AI*: why asking for JSON "please" in the prompt isn't enough, how a record C# becomes a *JSON Schema* that constrains the model, and a ==tutorial Minimal API .NET== where a local LLM with *Ollama* triages help desk tickets returning ==typed objects==, then saved to *MongoDB*.
**Function calling** with *Microsoft.Extensions.AI*: what it is, when to use it (and how it differs from *RAG*), and a ==.NET Minimal API tutorial== where a local LLM running on *Ollama* calls your own C# methods — reading orders from *MongoDB* and prices from an external API with *Refit*.
==AI agent orchestration== is the pattern that in 2026 is replacing the single agent for complex tasks. Here I walk through the **4 patterns** (hierarchical, collaborative, competitive, hybrid), the **frameworks** that matter — from *LangGraph* and *CrewAI* to the *Microsoft Agent Framework* — and a **hands-on ASP.NET Core example** that orchestrates four specialised agents in a few lines.
==MCP transport protocols== explained in depth: **stdio** for local servers and **Streamable HTTP** for remote ones, with a note on the deprecated *HTTP+SSE*. When to pick *one over the other*, how **authentication** works (OAuth 2.1), and two **C# .NET** examples with *Microsoft Agent Framework* and *Microsoft.Extensions.AI*: one stdio without auth (Filesystem) and one HTTP with auth (GitHub).
**RAG** explained from scratch: what it is, the *types* (hybrid, graph, re-ranking, agentic), which **vector databases** to choose — with Docker or cloud — and a ==Minimal API .NET tutorial== using *Ollama* for the LLM and embeddings and *MongoDB Community 8.2* for vectors and hybrid search.