Getting Started: AI for .NET Developers
5h 11m 32s
English
Paid
The world of artificial intelligence and large language models is developing at a dizzying pace: tools and approaches are changing so rapidly that just after mastering one technology, the next one appears on the horizon. In such an environment, it is especially important to have a good understanding of the basics to be able to adapt and confidently progress further.
In this course, Ed will systematically introduce you to AI and LLM, starting with the most basic concepts, so you can lay a solid foundation for further learning. You will understand the modern AI landscape and available solution options, master prompt engineering, get acquainted with AI libraries for .NET, and clarify complex topics like LLM tokens.
Throughout the course, you will learn to work with text, streaming responses, and images, create a basic AI agent, and explore the application of RAG using the new Agent Framework from Microsoft. As a result, you will gain a comprehensive understanding of the AI ecosystem and practical skills necessary for a confident start and further growth in this field as a .NET developer.
About the Author: Dometrain
Courses crafted by real engineers for the real world. Dometrain courses aim to provide the training experience software engineers would get by pair-programming with a very senior and skilled engineer in a real modern company.
Watch Online 55 lessons
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/ #1: Welcome
All Course Lessons (55)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Welcome Demo | 01:06 | |
| 2 | What will you learn in this course? | 01:29 | |
| 3 | Who is the course for and prerequisites | 00:54 | |
| 4 | A brief history of AI and .NET | 04:54 | |
| 5 | Deterministic vs. non-deterministic programming | 09:16 | |
| 6 | A developer's guide to AI, ML, and Deep Learning | 08:07 | |
| 7 | Exploring LLM and GenAI Concepts | 02:02 | |
| 8 | Providers and Hosting Options | 03:27 | |
| 9 | An overview of Azure AI Foundry | 04:26 | |
| 10 | Deploying your first Large Language Model | 03:49 | |
| 11 | An introduction to Chat Playground | 05:05 | |
| 12 | Exercies introduction | 03:42 | |
| 13 | Summarization prompts | 02:45 | |
| 14 | Categorization prompts | 04:14 | |
| 15 | Sentiment analysis prompts | 06:48 | |
| 16 | Language translation prompts | 07:56 | |
| 17 | Creating structured output from unstructured data | 07:59 | |
| 18 | Using LLMs to generate and refine summarization prompts | 07:05 | |
| 19 | Using LLMs to generate and refine categorization prompts | 08:02 | |
| 20 | Using LLMs to troubleshoot prompt responses | 06:52 | |
| 21 | Understanding the three pillars of AI in .NET | 03:54 | |
| 22 | When to use what library and why | 06:49 | |
| 23 | Introduction to building a Tokenizer | 03:49 | |
| 24 | Token Visualizer project dependencies | 04:02 | |
| 25 | Connecting the visualizer component | 04:04 | |
| 26 | Performing tokenization and reviewing the output | 08:21 | |
| 27 | Microsoft.Extensions.AI Exercise Introduction | 01:35 | |
| 28 | Configuring and establishing application settings with user secrets | 10:42 | |
| 29 | Configuring DI with AI Dependencies and IChatClient | 06:37 | |
| 30 | Creating a chat application background service | 05:23 | |
| 31 | Chat completions using ChatMessage and ChatRepsonse | 06:44 | |
| 32 | Creating a chat loop | 06:29 | |
| 33 | Adding a basic conversational memory | 05:10 | |
| 34 | Creating a Web Agent by Extending the ChatApp | 10:39 | |
| 35 | Reading Web Resources for Context Augmented Generation | 07:36 | |
| 36 | Blazor Project Intro | 02:17 | |
| 37 | IChatClient Dependencies and Configuration | 08:19 | |
| 38 | Chat UI setup | 07:34 | |
| 39 | AIContent and content types | 03:03 | |
| 40 | Using image content with multimodal model input | 10:29 | |
| 41 | How to enable logging on IChatClient | 05:23 | |
| 42 | How to use GetStreamingResponseAsync to stream text from IChatClient | 08:12 | |
| 43 | Basic Agent Intro | 03:19 | |
| 44 | Structured Output with GetResponseAsync<T> | 11:58 | |
| 45 | Agent Framework Introduction | 03:51 | |
| 46 | Agent Setup and Project Configuration | 06:57 | |
| 47 | Creating OpenAI Clients | 03:59 | |
| 48 | Creating an OpenAI Vector Store | 06:37 | |
| 49 | Creating an Agent with Agent Framework | 05:17 | |
| 50 | Agent Conversation UI | 05:44 | |
| 51 | Azure OpenAI Files and Vector Store | 02:14 | |
| 52 | Running an Agent | 08:49 | |
| 53 | Citations and Annotations | 09:32 | |
| 54 | Agent Framework Threads | 05:11 | |
| 55 | Conclusion | 00:55 |
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