Foundations of Open Generative AI Engineering

Generative AI is transforming technology, but relying solely on closed, proprietary APIs limits your control, privacy, and customization. Open-source models offer a powerful, flexible alternative for developers wanting to build independent, secure, and cost-effective AI applications. This text-based course guides you through the core principles of open generative AI engineering, taking you from fundamental concepts to deploying your own localized solutions. By reading through clear explanations and structured code examples, you will gain the confidence to select, run, and adapt open-source models for real-world scenarios. What you'll learn: - Understand the core architecture of large language models and the open-source AI ecosystem - Configure and run open-source models locally using modern tools and libraries - Apply prompt engineering techniques to optimize model outputs for specific tasks - Implement Retrieval-Augmented Generation (RAG) using vector databases to ground model responses in custom data - Explore the fundamentals of fine-tuning open models on custom datasets safely and efficiently - Evaluate model performance, license types, and deployment strategies for production environments We begin with foundational terminology, exploring how open-source weights differ from closed APIs, before moving into step-by-step written guides on model selection, local execution, and integration patterns. This course is designed for software developers, data enthusiasts, and tech professionals new to AI engineering, requiring no prior machine learning background. Start reading today to master the essentials of open-source generative AI.