Introduction to Generative AI Engineering with LLMs

Generative artificial intelligence is transforming how we build software, but harnessing the power of large language models requires a solid engineering foundation. This course introduces you to the core concepts and practical workflows needed to design and deploy LLM-powered applications. By reading through our structured lessons, you will transition from a curious learner to a capable AI practitioner. You will understand how these models function under the hood, how to interact with them programmatically, and how to architect systems that deliver reliable, context-aware results. What you'll learn: - Understand the fundamental architecture and training processes of Large Language Models (LLMs). - Apply effective prompt engineering techniques to guide model behavior and optimize outputs. - Configure Retrieval-Augmented Generation (RAG) workflows to connect LLMs with external data sources. - Utilize vector databases to store and retrieve semantic embeddings efficiently. - Implement basic LLM evaluation and monitoring strategies to ensure application quality. - Design secure and scalable application architectures that integrate generative AI APIs. The course begins with essential terminology and the mechanics of transformer architectures, then progresses to API integration, prompt design, and advanced retrieval patterns. You will build your understanding step-by-step through clear written explanations and practical code examples. This course is designed for software developers, product managers, and technology enthusiasts who are new to generative AI engineering. No prior experience with machine learning is required, though a basic understanding of programming concepts is helpful. Start reading today to begin building the next generation of intelligent applications.