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7 tutorials
Learn step-by-step how to build a RAG application using DeepSeek R1, OLLAMA, and Microsoft Semantic Kernel. In this tutorial, we create an AI-powered expense manager that answers natural-language questions from your own documents.
In this step-by-step tutorial, you'll learn how to create our first AI Agent using Microsoft Semantic Kernel's Agentic Framework and .NET.
When building AI-native applications with Semantic Kernel, prompts are the instructions you give to an LLM like GPT-4 to generate intelligent responses.
Leverage the power of Microsoft Semantic Kernel to build smart, modular, and scalable AI agents with C#. This blog walks you through building your first AI agent using the Agentic Framework.
Microsoft’s Semantic Kernel (SK) enables developers to integrate powerful LLM capabilities like GPT-4 into real-world applications—using C#, Python, or Java. But what exactly is it, and where does it shine? Let’s dive in.
The Process Framework in Semantic Kernel helps developers codify AI behavior with precision, logic, and reuse. It enables building structured flows instead of just ad-hoc prompt chains — making your AI systems smarter, more maintainable, and enterprise-ready
If you're building AI-powered applications using Microsoft technologies, Semantic Kernel plugins are an essential concept to master. In this SEO-optimized guide, we’ll break down what plugins are, why they matter, and how to use them to supercharge your AI apps using .NET and OpenAI.
