GenAI for .NET: Build LLM Apps with OpenAI and Ollama | Udemy [Update 07/2025]
English | Size: 2.9 GB
Genre: eLearning[/center]

Develop Chat, Vector Search, VectorDB, RAG and EShop AI Apps using OpenAI, Ollama and Microsoft Extensions AI (MEAI)

What you will learn
GenAI Concepts: LLM, Token, SLM, Prompt Engineering
.NET + AI ecosystem: AI Development Tools and Libraries for .NET
Setup LLM Providers: GitHub Models, Ollama, Azure AI Foundry
Chat, Text Completions, Analysis and Function Calling w/ .NET
Text Completion LLM with GitHub Models OpenAI gpt-5-mini model
Classification, Summarization, Sentiment Analysis LLM Other Use Cases
Structured Output in LLM for Data Extraction Use Case
Build AI Chat App with .NET and gpt-5-mini model
Invoke .NET functions using GH gpt-5-mini model with Function Calling
.NET AI Vector Search using Vector Embeddings and Vector Store
Generate Embeddings and Calculate Similarity w/ CosineSimilarity
Develop .NET AI Vector Search App w/ Ollama and all-minilm embedding model
Retrieval Augmented Generation (RAG) Application w/ .NET AI
Build .NET Chat App w/ RAG Template w/ OpenAI gpt-5-mini model
Build .NET Chat App w/ RAG Template using Ollama and all-minilm
Build Image Analysis App w/ .NET and GH Models - OpenAI gpt-5-mini
Build Image Analysis App w/ .NET and Ollama llava
Build Eshop Vector Search App w/ .NET Aspire, gpt-5-mini and Qdrant Vector DB
Add Qdrant Vector Database into .NET Aspire
Unified AI building blocks: Microsoft Extensions AI (MEAI)

In this hands-on course, you'll learn to integrate OpenAI, Ollama and .NET's new Microsoft-Extensions-AI (MEAI) abstraction libraries to build a wide range of GenAI applications-from chatbots and semantic search to Retrieval-Augmented Generation (RAG) and image analysis.

Throughout the course, you'll learn:

.NET + AI Ecosystem

You'll learn about Microsoft's new abstraction libraries like Microsoft-Extensions-AI, which makes it super easy to integrate & switch different LLM providers like OpenAI, Azure AI, Ollama and even self-hosted models.

Setting Up LLM Providers

Configure the LLM providers-such as GitHub Models, Ollama, and Azure AI Foundry-so you can choose the best fit for your use case.

Text Completion LLM w/ GitHub Models OpenAI gpt-5-mini and Ollama llama3.2 Model model

You'll learn how to use .NET to integrate LLM models and performs Classification, Summarization, Data extraction, Anomaly detection, Translation and Sentiment Analysis use cases.

Build AI Chat App with .NET and gpt-5-mini model

You'll develop back-and-forth conversation based messaging with LLM and user where the AI maintains context across multiple user turns.  We will use Chat Streaming features when developing AI Chat Application.

Function Calling with .NET and gpt-5-mini model

Develop a function that will trigger from OpenAI GPT-5-mini. The model returns structured JSON specifying which .NET function to invoke, along with arguments for retrieving real-time data.

.NET AI Vector Search using Vector Embeddings and Vector Store

We'll also cover Vector Search, a powerful feature that allows semantic search based on meaning-not keywords.

You'll learn how to:

Generate embeddings using OpenAI's text-embedding-3-small or Ollama's all-MiniLM  embeddings model,

Store these in a vector database like Qdrant

Query the vector store with user embedding to find top matches by similarity

Retrieve relevant data based on similarity searches-all in our .NET applications.

RAG - Retrieval-Augmented Generation with .NET

You'll learn how to combine vector search results with LLM responses to:

Retrieve relevant data from your own sources

Break documents into chunks → embed them → store in vector DB

At query time, embed the question → retrieve relevant chunks → pass them along with the user's query to the LLM

Get accurate, context-specific answers using your internal data from LLM

We'll implement the full RAG flow with real examples using .NET and Qdrant.

Image Analysis with .NET AI

Cover image recognition and analysis, showing how to send images to AI models, receive tags, captions or visual summaries and integrate those capabilities directly into your .NET apps

Vision models for object recognition, classification, or captioning

Combining text and image processing to build more powerful, multi-modal applications for traffic cam analysis operations

Final Project: E-Shop Semantic Search with .NET Aspire

You'll build a complete full-stack AI-powered EShop Vector Search app step by step.

We'll use:

.NET Aspire for service orchestration

Qdrant as our Vector Database

and GPT-5 Mini or Ollama's local models to generate embeddings and respond intelligently to user queries

In this project, you'll:

Generate product embeddings with OpenAI text-embeddings or Ollama all-minilm

Store them in Qdrant Vector DB  for fast similarity search

Implement a RAG flow that provides semantic search over our EShop product catalog

Enable users to search products by meaning-not just keywords

This project brings everything you learn in this course into a single, full-stack, real-world app.

By the end of this course, you'll have the tools and confidence to build intelligent, GenAI-powered apps in .NET.

Who this course is for:
Developers and Architects who curios about developing LLM Applications with .NET

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