Building LLM Applications with LangChain

Large language models are transforming software, but connecting them to real-world data and workflows requires a robust framework. LangChain provides the essential tools to bridge this gap, allowing you to build intelligent, context-aware applications. Through this text-based course, you will transition from writing simple prompts to constructing sophisticated, multi-step LLM systems that remember past interactions and query external data sources. What you'll learn: Understand foundational LLM concepts and how LangChain structures application workflows; Manage prompts, parse model outputs, and maintain conversational state with memory modules; Build sequential and conditional chains to link multiple LLM calls together; Implement Retrieval-Augmented Generation (RAG) using vector databases for question-answering systems; Create autonomous agents that decide which tools to use based on user input; Apply basic LLM observability and evaluation techniques to monitor your application's performance. We begin with key terminology and basic prompt orchestration before advancing to complex chains, data retrieval, and agentic workflows. You will read clear explanations, analyze production-ready code snippets, and complete written exercises to solidify your understanding. This course is designed for software developers and tech enthusiasts who are new to LLM development and want a structured, conceptual introduction without complex prerequisites. Start reading today to master the core patterns of modern LLM application development.