Generative AI Engineering with Azure

Generative artificial intelligence is transforming how we build software, but moving from simple prompts to production-ready AI applications requires solid engineering principles. This text-based course guides you through the foundational concepts of designing, deploying, and managing intelligent systems on cloud infrastructure. You will transition from an AI enthusiast to a foundational generative AI engineer. By reading clear architectural breakdowns, studying structured code examples, and working through conceptual design exercises, you will understand how to integrate large language models into scalable applications, implement search-retrieval patterns, and maintain model performance safely. What you'll learn: - Understand the core architectures of large language models and generative AI systems - Configure Azure OpenAI Service instances and manage API deployments securely - Apply prompt engineering techniques and system instructions to guide model behavior - Implement Retrieval-Augmented Generation (RAG) using vector search and databases - Fine-tune pre-trained models for specific domain-specific tasks and datasets - Evaluate model outputs and establish basic LLMOps workflows for continuous monitoring The course begins with essential AI definitions and model mechanics before guiding you through API integration, prompt orchestration, and vector-based search. You will then explore advanced patterns like RAG, fine-tuning concepts, and responsible AI guardrails. This program is designed for software developers, cloud enthusiasts, and technology professionals new to AI engineering, requiring no prior machine learning background. Start reading today to build your foundation in cloud-based generative AI engineering.