Build, Analyze, and Refactor LLM Workflows with LangChain

Large Language Models are powerful, but turning them into reliable, production-ready applications requires structured workflows. This text-based course guides you through the process of building, evaluating, and refactoring LLM-powered applications using LangChain. By reading the clear explanations and analyzing practical code examples, you will transition from writing simple prompts to engineering robust, maintainable LLM pipelines. You will understand how to debug, optimize, and restructure your workflows to ensure they are efficient, reliable, and ready for real-world deployment. What you'll learn: - Understand foundational LLM concepts and LangChain architecture basics. - Build structured workflows using chains, prompts, and output parsers. - Implement retrieval-augmented generation (RAG) patterns to connect models to external data. - Analyze and debug application performance using modern observability concepts. - Refactor legacy or fragile LLM code into modular, maintainable components. - Apply prompt engineering best practices directly within programmatic pipelines. The journey begins with foundational LLM terminology and basic LangChain setups before moving into practical workflow construction. You will then explore advanced patterns like RAG and learn how to systematically analyze and refactor your code for better performance and maintainability. This course is designed for software developers, data enthusiasts, and tech professionals who are new to LangChain and want a structured, text-focused introduction to building LLM applications. No prior experience with AI frameworks is required, though basic Python knowledge is helpful. Start reading today to master the essential patterns of modern LLM application development.