Generative AI Developer: Build RAG, AI Agents, and Cloud Deployments

Generative AI is transforming how we build software, but moving beyond simple prompts requires a deep understanding of system architecture. This course bridges the gap between conceptual AI and practical engineering, guiding you through the process of building production-grade AI applications. You will transition from understanding how Large Language Models work to designing autonomous agent systems and semantic search engines. By reading through structured explanations and analyzing clean code implementations, you will gain the skills needed to design, optimize, and run AI workloads in the cloud. What you'll learn: - Understand the core concepts of Generative AI, Large Language Models, and the Transformer architecture - Build robust Retrieval-Augmented Generation (RAG) pipelines for querying custom documents - Develop autonomous multi-agent workflows using modern frameworks like CrewAI and PydanticAI - Implement vector databases and semantic search to power intelligent knowledge retrieval - Configure and deploy AI models to cloud environments using Docker and containerization - Apply Python async patterns and type hints to write clean, production-ready AI orchestration code This course begins with key terminology, foundational definitions of neural networks, and prompt engineering basics. You will then progress through step-by-step written guides covering API integration, vector databases, multi-agent coordination, and cloud deployment strategies. This course is designed for software developers, system architects, and tech enthusiasts who want to enter the field of AI engineering. No prior machine learning background is required, though basic familiarity with Python is recommended. Start reading today to build and deploy your first intelligent AI system.