LangChain Foundations: Build Your First LLM Applications

Integrating large language models into software applications requires more than just sending API calls. LangChain provides the essential framework to orchestrate prompts, chain model interactions, and connect AI to external data sources. By taking this course, you will transform from an AI enthusiast into a developer capable of building structured, LLM-powered workflows. You will learn how to design reusable prompt templates, manage conversational history, and structure model outputs for real-world usability. What you'll learn: Understand the core architecture of LangChain and how it interacts with different large language models; Create dynamic prompt templates to guide AI behavior and generate consistent responses; Build sequential chains using modern LangChain Expression Language syntax to link multiple tasks; Manage conversational memory to keep track of user interactions in chat applications; Apply basic retrieval-augmented generation concepts to connect models to external text data; Structure model outputs into reliable formats like JSON for easy software integration. The course begins with essential AI terminology and foundational setup, then moves systematically through prompt management, chaining logic, and basic data retrieval. You will read detailed explanations and analyze practical code snippets designed to solidify your understanding of modern AI orchestration. This course is designed for software developers and tech enthusiasts who are new to LangChain and want a clear, step-by-step introduction to LLM integration. No prior experience with AI frameworks is required, though a basic understanding of Python is helpful. Start reading today to begin building your own intelligent text applications.