Developing AI-Powered Apps with Python and Azure AI Foundry

Introduction to Azure AI Foundry

Azure AI Foundry is a comprehensive platform that streamlines AI solution development and deployment. In this chapter, discover how to use hubs for building and testing AI solutions, projects for grouping and deploying AI apps, and tools for managing resources, all while ensuring responsible AI practices are followed.

• What is Azure AI Foundry?
• Collaborative AI Development
• Developing and Testing AI Solutions in Hubs
• Deploying AI Applications with Projects
• Managing Resources and Projects

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, etc. Azure AI Foundry brings together these services into a single, unified development environment.

• 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-4. 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

Model Context Protocol (MCP): Standardizing LLM Integrations

This chapter explores the Model Context Protocol (MCP), an open standard revolutionizing how applications provide context to LLMs. MCP acts as a 'USB-C for AI,' standardizing connections between LLMs and various data sources or tools. Crucially, MCP empowers companies to define, once and for all, precisely how their proprietary data and tools are utilized by AI systems.

• What is the Model Context Protocol (MCP)?
• Understanding MCP's Core Components: Hosts, Clients and Servers
• Defining MCP Prompts, Tools and Resources
• Building MCP Servers with Python

Deploying AI Models

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

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

Retrieval Augmented Generation with Azure AI Search

Azure AI Search enables the Retrieval Augmented Generation (RAG) design pattern, enhancing LLMs knowledge with your own company specific data. This chapter explores the RAG design pattern by incorporating Azure AI Search, into your LangChain/Prompt flow Python applications.

• What is Azure AI Search?
• Retrieval Augmented Generation with Prompt flow and LangChain
• Enhancing AI Models with your Own Data: Blog Storage, Azure SQL, OneLake...
• Hybrid Search with Semantic Reranking
• Use AI Enrichment to extract insights
• Fine-tuning vs RAG
• LAB: Chat with Azure OpenAI models using your own data

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 Python. 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

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

Building Agentic AI Systems

This chapter introduces building agentic AI systems. Learn what agents are, suitable use cases, essential design foundations including models, tools, and instructions, different orchestration patterns, and the importance of implementing robust guardrails and human oversight mechanisms.

• Introduction to Agentic AI Systems
• Identifying Ideal Use Cases for Agents
• Core Principles of Agent Design (Models, Tools, Instructions)
• Orchestrating Agent Workflows: Single and Multi-Agent Approaches
• Implementing Guardrails for Safe and Reliable Agents
• Integrating Human Oversight and Intervention Strategies

Testing and Moderating AI Models

How can you ensure an LLM provides relevant and coherent answers to users' questions using the correct info? How do you prevent an LLM from responding inappropriately? Discover the answers to these questions and more by exploring evaluation metrics in Azure AI Foundry and the Azure AI Content Safety Service.

• Ensuring Coherent and Relevant LLM Responses
• Utilizing Correct Information in AI Answers
• Preventing Inappropriate LLM Responses
• Exploring Custom Evaluation Metrics in Azure AI Foundry with Prompt flow
• Leveraging Azure AI Content Safety Service
• Enhancing AI Performance and Safety

Making your AI Apps Traceable

Ensuring your AI app behaves as expected doesn't end at deployment. It's crucial to monitor its interactions with users while it's running in production. Learn how Azure AI Foundry integrates with industry standards like OpenTelemetry to give you a clear and transparent view of your app's behavior.

• Monitoring AI App Interactions in Production
• Integrating OpenTelemetry with Azure AI Foundry
• Tracing and Debugging
• Capturing Model Calls and Latency Issues
• Setting Up Local Testing Environments

Fine-tuning AI Models with Azure AI Foundry

This chapter explores the advantages of fine-tuning pre-trained LLMs for higher accuracy and customized behavior compared to Retrieval Augmented Generation (RAG). While RAG offers dynamic updates and cost-effectiveness, fine-tuning provides superior precision for specialized tasks, making it ideal for achieving domain-specific results.

• Introduction to Fine-Tuning LLMs
• How to decide between Fine-Tuning and RAG?
• Using Task-Specific Data for Enhanced Performance
• Reducing Hallucinations with Fine-Tuning