LLM Engineering: Build and Deploy Large Language Model Applications

Large Language Models are transforming how we build software, but moving from simple prompt templates to robust, production-ready AI applications requires a structured engineering approach. This text-based course guides you through the fundamental concepts and practical architectures needed to design, build, and optimize LLM-powered systems. You will transition from a casual user of AI APIs to a capable LLM engineer who understands how to connect models to external data sources, manage system memory, and orchestrate complex workflows. What you'll learn: Understand core LLM concepts, tokenization, and how foundational models process text; Apply advanced prompt engineering techniques and structured output parsing for reliable model responses; Build Retrieval-Augmented Generation (RAG) pipelines to connect LLMs with external knowledge bases; Configure vector databases to store, index, and retrieve semantic information efficiently; Design multi-step agentic workflows and manage conversational state and memory; Implement basic LLMOps practices to monitor, evaluate, and secure your language model applications. The course starts with essential terminology and the mechanics of language models before moving step-by-step through API integration, data retrieval patterns, and system evaluation. Through clear written explanations and structured code walk-throughs, you will develop a deep mental model of AI application architecture. This program is designed for software developers, data enthusiasts, and tech professionals who are new to LLM engineering and want a solid, code-first foundation without complex mathematical prerequisites. Start reading today to build your first production-grade LLM application.