Building RAG Systems and AI Agents with Python and OpenAI

The shift from simple chatbots to sophisticated AI systems requires a deep understanding of how models interact with external data and perform autonomous tasks. This course teaches you how to bridge that gap by building robust, data-aware applications from the ground up. You will progress from foundational API interactions to creating complex multi-agent systems that can reason, plan, and execute. By the end of the course, you will be able to design and implement AI solutions that are both accurate and contextually relevant to specific business needs. What you'll learn: - Understand the fundamental architecture of Generative AI and Large Language Models. - Build Retrieval-Augmented Generation (RAG) systems to ground AI responses in factual data. - Implement vector databases and semantic search for efficient information retrieval. - Design autonomous AI agents that use specialized tools to complete multi-step workflows. - Practice evaluating AI performance using modern metrics for context and factual accuracy. - Apply orchestration frameworks to manage collaboration between multiple AI agents. The course begins with essential terminology and the basics of prompt engineering before moving into the technical implementation of vector stores and agentic logic. You will learn through clear written explanations and practical code examples designed for immediate application. This course is for beginners who have a basic grasp of Python and want to specialize in AI development. No prior experience with machine learning or AI APIs is required. Begin your journey into the world of agentic AI and advanced retrieval systems.