Beginner
AI in .NET: The Complete Landscape for Developers
Map the 2026 AI in .NET ecosystem: Microsoft.Extensions.AI, Agent Framework, Semantic Kernel, vector data, MCP, ML.NET, ONNX and Foundry, and when to use each.
AI & Machine Learning in .NET
Build intelligent .NET apps: Microsoft.Extensions.AI, Microsoft Agent Framework, Semantic Kernel, RAG, vector search, MCP, ML.NET, ONNX Runtime and Azure AI Foundry.
20 guides
AI is now a standard part of .NET development. These guides cover the full stack of AI engineering in C#. You will learn the unified Microsoft.Extensions.AI abstractions and how to build agents with the Microsoft Agent Framework and Semantic Kernel. You will ground models in your own data with RAG and vector search, expose tools through the Model Context Protocol, and run models locally with ONNX Runtime and small language models.
You will also find production topics that separate prototypes from real products: structured outputs, evaluation, observability, cost control, responsible AI and defenses against prompt injection.
Intermediate
Microsoft.Extensions.AI: Unified AI Abstractions for .NET
Master Microsoft.Extensions.AI: IChatClient, IEmbeddingGenerator, streaming, tool calling, middleware pipelines, DI registration, custom clients and testing.
Intermediate
Microsoft Agent Framework: Build AI Agents in C#
Build AI agents in C# with Microsoft Agent Framework 1.x: ChatClientAgent, sessions, tools, MCP, workflows, human approval, hosting and migration.
Intermediate
Semantic Kernel in Practice: Plugins, Functions and Orchestration
Semantic Kernel in practice for C#: kernel, plugins, prompt templates, automatic function calling, filters, vector search, agents, and its 2026 role.
Intermediate
Azure OpenAI and Microsoft Foundry for .NET Developers
Use Azure OpenAI in Microsoft Foundry (formerly Azure AI Foundry) from .NET: deployments, keyless auth, guardrails, quotas, PTUs, Agent Service and cost.
Intermediate
Using the Official OpenAI .NET Library for C#
A practical guide to the OpenAI .NET SDK: ChatClient, streaming, tool calls, structured outputs, the Responses API, embeddings, images, audio and retries.
Advanced
Retrieval-Augmented Generation (RAG) in .NET: End-to-End Guide
Build production RAG in C#, covering ingestion, chunking, embeddings, hybrid retrieval, reranking, grounded prompts, citations and evaluation in .NET.
Intermediate
Embeddings and Vector Databases in .NET: A Practical Guide
Learn how embeddings and vector databases work in .NET: similarity metrics, HNSW indexes, Microsoft.Extensions.VectorData and pgvector, SQL Server and Qdrant.
Intermediate
Function Calling and Tool Use with LLMs in C#
Master function calling in C# with Microsoft.Extensions.AI: AIFunctionFactory, FunctionInvokingChatClient, parallel calls, approvals, security and tests.
Advanced
Model Context Protocol (MCP) in C#: Build Servers and Clients
Build Model Context Protocol servers and clients in C# with the official SDK 2.x: tools, resources, Streamable HTTP, OAuth, security and VS Code setup.
Intermediate
ML.NET: Practical Machine Learning for .NET Developers
Learn ML.NET in C#: MLContext, IDataView, pipelines, trainers, AutoML, metrics, PredictionEnginePool, ONNX, and when to pick ML.NET over LLMs.
Advanced
Local AI in .NET: ONNX Runtime, Ollama and Foundry Local
Run LLMs locally in .NET with ONNX Runtime GenAI, Ollama and Foundry Local: small language models, GPUs and NPUs, quantization and IChatClient.
Intermediate
Multimodal AI in .NET: Vision, Audio and Speech
Build multimodal .NET apps that understand images, documents, speech and audio, and generate images and voice, with Microsoft.Extensions.AI and Azure AI.
Beginner
Prompt Engineering for .NET Developers
A practical guide to prompt engineering for .NET developers, covering prompt anatomy, few-shot examples, reasoning models, templates and injection risks.
Intermediate
Structured Outputs: Reliable JSON from LLMs in C#
Get reliable JSON from LLMs in C#: JSON mode vs structured outputs, typed Microsoft.Extensions.AI responses, JSON Schema from types, validation and retries.
Advanced
Evaluating AI Applications in .NET: Quality, Safety and CI
Evaluate LLM apps in .NET with Microsoft.Extensions.AI.Evaluation: quality and safety evaluators, golden datasets, LLM-as-judge, caching, reports and CI gates.
Advanced
AI Agent Architecture Patterns for .NET: Orchestration, Memory and A2A
Design AI agents in .NET: single vs multi-agent, sequential, concurrent, handoff, group chat and Magentic orchestration, memory, approvals, durability and A2A.
Intermediate
Responsible AI and LLM Security for .NET Applications
Secure LLM apps in .NET: OWASP Top 10 for LLMs 2026, prompt injection defenses, Prompt Shields, PII redaction, output checks, least privilege and the EU AI Act.
Advanced
Observability and Cost Control for LLM Apps in .NET
Learn observability and cost control for .NET LLM apps, covering OpenTelemetry GenAI conventions, token tracking, caching, model routing and 429 retries.
Beginner
AI-Assisted .NET Development with GitHub Copilot and Coding Agents
How .NET teams use GitHub Copilot, coding agents, custom instructions and MCP servers, plus review, security and modernization guidance for 2026.
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