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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.

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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.

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#1: Welcome
All Course Lessons (55)
#Lesson TitleDurationAccess
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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