Developing AI-Powered Apps with C# and Azure AI

What is Artificial Intelligence?

In this chapter you will get a short overview about what AI is exactly, and what we can do with it.

• Definitions of Artificial Intelligence
• Machine Learning Basics
• Domains of Artificial Intelligence
• History, Current State and Future

Ready-to-Use AI Models with Azure AI Services

Azure AI services provides a comprehensive suite of out-of-the-box and customizable AI tools, APIs, and pre-trained models that detect sentiment, recognize speakers, understand pictures and many more.

• Azure AI Services Overview
• Azure AI Language
• Azure AI Vision
• Azure AI Speech
• Azure AI Document Intelligence

Azure OpenAI and Large Language Model Fundamentals

This module introduces Azure OpenAI and the GPT family of Large Language Models (LLMs). You'll learn about available LLM models, how to configure and use them in the Azure Portal, and the Transformer architecture behind models like GPT-4o. The latest GPT models offer Function Calling, enabling connections to external tools, services, or code, allowing the creation of AI-powered Copilots. Additionally, you'll discover how Azure OpenAI provides a secure way to use LLMs without exposing your company's private data.

• Introducing OpenAI and Large Language Models
• The Transformer Model
• What is Azure OpenAI?
• Configuring Deployments
• Understanding Tokens
• LLM Pricing
• Azure OpenAI Chat Completions API
• Role Management: System, User and Assistant
• Azure OpenAI SDK
• Extending LLM capabilities with Function Calling
• LAB: Deploying and Using Azure OpenAI

Orchestrating AI Models using Semantic Kernel

Semantic Kernel is an open-source SDK backed by Microsoft that seamlessly integrates Large Language Models such as OpenAI and Azure OpenAI with programming languages like C#. It allows users to use natural language input within Large Language Models to seamlessly invoke and interact with your custom code.

• An Introduction to Semantic Kernel
• Integrating LLMs in your applications
• Keeping track of Token Usage
• Enable AI Models to execute code using Plugins
• Control AI Models with Filters
• Best practices for dependency injection in managing AI services
• Observable AI Apps with OpenTelemetry
• LAB: Create a Natural Language to SQL Translation Copilot

Retrieving Semantically Related Data with Vector Search

Vector search is a powerful technique that allows you to retrieve semantically related data from large datasets such as company documents or databases. This chapter will teach you how vector search works and how it enables you to find relevant information without depending on exact keyword based search terms or language of the information in the dataset.

• Capture Semantic Meaning with Embeddings
• Vector Search
• Vector Search Design Considerations

Using your own data in a LLM with Azure AI Search

Azure AI Search facilitates the adoption of the Retrieval Augmented Generation (RAG) design pattern. This methodology involves retrieving pertinent information from a data source and using it to increase the knowledge of generative AI models.This combination of retrieval and generation sets a new standard for AI-driven search solutions.

• What is Azure AI Search?
• Retrieval Augmented Generation
• Creating an Index on your Own Data
• AI Enrichment with your own Data
• Using the Azure OpenAI SDK
• Privacy Concerns
• Fine-tuning vs RAG
• LAB: Chat with Azure OpenAI models using your own data

Prompt Engineering and Design Patterns

In this chapter, you'll explore advanced techniques allowing you to control the model's output, transforming generic responses into precise, valuable results. Additionally the chapter covers emerging design patterns in the field of Gen AI app development that help you increase quality of model responses and reduce costs.

• What is Prompt Engineering?
• Few-Shot Prompting
• Structured Query Generation
• Verifying Model responses with Hallucination Detection
• Saving costs with Semantic Caching

Deploying AI Models on Azure AI Foundry

The cost and quality of your AI-powered app depend largely on your choice of AI model and how you deploy it. Learn about the available model catalog, featuring state-of-the-art Azure OpenAI models and open-source models from Hugging Face, Meta, Google, Microsoft, Mistral, and many more.

• Model Catalog Overview
• Model Benchmarks
• Selecting the Best Deployment Mode

Working with Open-Source Language Models

This chapter empowers you to bring powerful AI capabilities to end-user environments like mobile devices, personal computers and browsers, enhancing scalability, costs and performance. Additionally you will learn how to deploy and host your own open-source Language Models in the form of an API that you have full control over.

• The Phi-3 Family of Small Language Models
• Deploying AI Models on Mobile and Edge Devices with ONNX Runtime
• Hosting and Deploying Language Models on-prem and in the cloud with Ollama

Building on the Microsoft Copilot Ecosystem

While building a complete AI-powered application from scratch can be beneficial, it is sometimes not the most efficient approach. In this chapter, you will learn the basics of extending the capabilities and knowledge of Copilot for Microsoft 365, allowing you to enhance its functionality and leveraging its robust and secure infrastructure and UI.

• Overview of Copilot for Microsoft 365 Extensibility Options
• Overview of Copilot Studio
• Extending Copilot's knowledge with Graph Connectors
• Allowing Microsoft Copilot to call REST API's
• Expanding Copilot Capabilities via Teams Message Extensions