Welcome
What will you learn in this course?
Who is the course for and prerequisites
Getting Started: AI for .NET Developers
Get started with integrating AI into your .NET applications effectively using the latest LLM best practices.
About This Course
The world of Artificial Intelligence and LLMs is moving at a breakneck pace, and tooling is changing so quickly that by the time you learn something, there is something new out there that you have to learn. Understanding the basics is crucial to learn how to adapt and evolve in an ever-changing field. In this course, Ed will introduce you to AI and LLMs from the very basics, so you can get started with a solid foundation on your AI journey. He will then explain the AI landscape and all the options available to you, move to prompt engineering, and cover the available AI libraries for .NET, as well as concepts like LLM Tokens, which can be very confusing. You will learn how to work with text, streaming responses, and image content types, as well as build a basic agent and use RAG with Microsoft’s brand-new Agent Framework.
Course Curriculum
5h 12m 11 sections
A brief history of AI and .NET
Deterministic vs. non-deterministic programming
A developer's guide to AI, ML, and Deep Learning
Exploring LLM and GenAI Concepts
Providers and Hosting Options
An overview of Azure AI Foundry
Deploying your first Large Language Model
An introduction to Chat Playground
Exercies introduction
Summarization prompts
Categorization prompts
Sentiment analysis prompts
Language translation prompts
Creating structured output from unstructured data
Using LLMs to generate and refine summarization prompts
Using LLMs to generate and refine categorization prompts
Using LLMs to troubleshoot prompt responses
Understanding the three pillars of AI in .NET
When to use what library and why
Introduction to building a Tokenizer
Token Visualizer project dependencies
Connecting the visualizer component
Performing tokenization and reviewing the output
Microsoft.Extensions.AI Exercise Introduction
Configuring and establishing application settings with user secrets
Creating a chat application background service
Configuring DI with AI Dependencies and IChatClient
Chat completions using ChatMessage and ChatRepsonse
Creating a chat loop
Adding a basic conversational memory
Creating a Web Agent by Extending the ChatApp
Reading Web Resources for Context Augmented Generation
Blazor Project Intro
IChatClient Dependencies and Configuration
Chat UI setup
AIContent and content types
Using image content with multimodal model input
How to enable logging on IChatClient
How to use GetStreamingResponseAsync to stream text from IChatClient
Basic Agent Intro
Structured Output with GetResponseAsync<T>
Agent Framework Introduction
Agent Setup and Project Configuration
Creating OpenAI Clients
Creating an OpenAI Vector Store
Creating an Agent with Agent Framework
Agent Conversation UI
Azure OpenAI Files and Vector Store
Running an Agent
Citations and Annotations
Agent Framework Threads
Conclusion
Meet Your Instructor
Ed Charbeneau
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