LLM Engineering: Build and Deploy Applications with Large Language Models

Large Language Models are transforming how we build software, but moving from simple prompts to reliable production systems requires solid engineering principles. This text-based course guides you through the foundational concepts and practical architectures needed to build robust, AI-driven applications. By completing this course, you will transition from an AI enthusiast to an LLM engineer capable of structuring data, managing context, and deploying intelligent systems. You will learn how to connect models to external data sources, optimize prompt performance, and evaluate system outputs systematically. What you'll learn: - Understand the core architecture of Large Language Models and key terminology in generative AI - Implement Retrieval-Augmented Generation (RAG) patterns to ground models in custom external data - Configure and query vector databases for efficient semantic search and retrieval - Design structured prompt templates and apply systematic prompt engineering techniques - Evaluate and monitor model outputs to ensure reliability, safety, and performance in production - Apply basic MLOps concepts to manage model lifecycle, latency, and API integration The course starts with essential terminology and the mechanics of text generation before guiding you through hands-on architectural patterns. You will progress from simple API calls to complex, data-connected systems through clear written explanations and detailed code snippets. This course is designed for software engineers, data professionals, and tech enthusiasts looking to enter the AI field. No prior experience with artificial intelligence is required, though basic Python programming knowledge is helpful. Start reading today to master the engineering patterns behind modern AI applications.