Azure OpenAI Service Developmentin Ras Al Khaimah

Module 1: Introduction to Azure OpenAI Service

• Overview of Azure OpenAI Service and Generative AI.
• Key capabilities and enterprise use cases.
• Understanding large language models (LLMs) and their applications.
• Introduction to Azure OpenAI architecture and responsible AI principles in Ras Al Khaimah, United Arab Emirates.

Module 2: Microsoft Azure Environment Setup

• Introduction to Azure subscriptions, resource groups, and Azure AI resources.
• Understanding model availability, access requirements, and deployment prerequisites.
• Configuring development environments and required tools.
• Managing access permissions and resource settings.

Module 3: Azure OpenAI API Fundamentals

• Understanding REST APIs, HTTP requests, and JSON responses.
• Connecting applications to Azure OpenAI endpoints.
• Authentication, API keys, and Microsoft Entra ID concepts.
• Handling API requests, responses, and common errors.

Module 4: Python Integration with Azure OpenAI

• Setting up Python and the required SDK packages.
• Connecting Python applications to Azure OpenAI deployments.
• Sending requests and processing model responses.
• Creating reusable functions for AI application development.

Module 5: Working with AI Models and Deployments

• Understanding supported model capabilities and deployment options.
• Configuring model parameters and request settings.
• Managing token limits and response lengths.
• Selecting appropriate models for application requirements.

Module 6: Prompt Engineering and Message Design

• Writing clear and effective prompts.
• Structuring system instructions and user messages.
• Using context and examples to guide model responses.
• Evaluating and refining prompts for consistent results.

Module 7: Building Conversational AI Applications

• Developing chatbot and virtual assistant workflows.
• Managing conversation history and context.
• Implementing multi-turn conversations.
• Designing user-friendly conversational experiences.

Module 8: Structured Outputs and Data Processing

• Generating structured responses for application workflows.
• Extracting relevant information from unstructured text.
• Working with JSON data and output validation.
• Applying classification and summarisation techniques.

Module 9: Tool Calling and Function Integration

• Understanding tool-calling concepts and supported model capabilities.
• Defining application functions and tool schemas.
• Connecting model responses to application-side functions.
• Validating tool inputs and handling execution errors.

Module 10: Retrieval-Augmented Generation (RAG)

• Understanding RAG architecture and business applications.
• Preparing, chunking, and organising documents.
• Exploring embeddings, vector search, and retrieval concepts.
• Grounding model responses in relevant source information.

Module 11: Enterprise Data and Application Integration

• Connecting AI applications to approved data sources.
• Designing context-aware question-answering workflows.
• Integrating AI capabilities into existing application components.
• Applying data validation and access controls.

Module 12: Security, Governance, and Responsible AI

• Protecting API credentials and sensitive information.
• Understanding identity, access permissions, and secure configuration.
• Exploring content filtering and responsible AI considerations.
• Applying privacy, compliance, and human-review practices where appropriate.

Module 13: Error Handling, Cost, and Performance Management

• Handling API errors, timeouts, and rate limits.
• Understanding token consumption and usage monitoring.
• Improving request efficiency and response reliability.
• Testing application performance and troubleshooting issues.

Module 14: Testing, Deployment, and Monitoring

• Testing AI application functionality and response quality.
• Validating outputs against defined requirements.
• Preparing applications for deployment.
• Monitoring application behaviour and maintaining configurations.

Module 15: Hands-On Capstone Project

• Define a business problem suitable for an Azure OpenAI solution.
• Design and develop a Python-based AI application.
• Integrate prompts, API requests, and response validation.
• Test the application for functionality, security, and reliability.
• Present the completed project and document key implementation decisions.