Vertex AI and Python: Build LLM and Generative AI Applications

Large language models are transforming how we build software, but integrating them securely and scalably requires the right cloud infrastructure. This text-based course guides you through leveraging powerful language models using Vertex AI and Python. You will transition from understanding basic generative AI concepts to implementing production-ready language model workflows. You will learn how to control model outputs, manage application state, and connect models to external data sources using modern developer practices. What you'll learn: - Understand foundational LLM concepts, tokenization, and cloud-based AI architecture. - Configure Vertex AI models using Python to adjust parameters like temperature and top-k. - Master prompt engineering techniques including zero-shot, few-shot, and systematic context injection. - Implement text embeddings and similarity searches to build basic Retrieval-Augmented Generation (RAG) patterns. - Develop practical applications such as automated summarizers and intelligent translation tools using clean Python code. - Apply modern Python practices like type hints and structured API calls for robust AI integrations. Starting with key terminology and model mechanics, the text-based lessons guide you step-by-step through console configuration, prompt design, and programmatic implementation. You will explore practical code examples that demonstrate how to connect cloud AI services to real-world software workflows. This course is designed for beginner developers, software engineers, and AI enthusiasts who want to start working with cloud-based language models. No prior experience with artificial intelligence or cloud platforms is required, though a basic understanding of Python is helpful. Start reading today to build your first cloud-powered AI application.