Generative AI Engineering: Building Modern LLM Applications

Generative artificial intelligence is transforming how we build software, but moving from simple prompts to robust engineering requires a structured foundation. This text-based course guides you through the core concepts and practical workflows needed to design and deploy generative AI systems. You will transition from a curious developer to a generative AI practitioner capable of building smart applications. You will understand how large language models work under the hood, how to programmatically interact with them, and how to connect them to external data sources. What you will learn: 1. Learn the foundational concepts of large language models and transformer architectures. 2. Master prompt engineering techniques to programmatically guide model outputs. 3. Build retrieval-augmented generation (RAG) pipelines to connect AI models to private vector databases. 4. Understand how to orchestrate complex AI workflows using modern application frameworks like LangChain. 5. Apply basic MLOps principles to evaluate, monitor, and secure your applications. 6. Practice writing clean Python code to integrate APIs and manage model context windows. The course starts with essential terminology and the mechanics of transformer models before moving into hands-on implementation. You will explore practical code patterns for prompt orchestration, document embedding, and semantic search through clear, written explanations. This course is designed for software developers, data enthusiasts, and technology professionals who want to enter the field of AI engineering. No prior experience with artificial intelligence is required, though a basic understanding of Python is helpful. Start your journey into generative AI engineering today and learn to build intelligent applications from the ground up.