Building LangChain Runnables and Custom Chains

Building powerful AI applications requires more than just sending isolated queries to a language model. To create truly intelligent workflows, you need to seamlessly connect prompts, models, and data processors into cohesive, repeatable chains. This text-only course guides you through the foundational mechanics of LangChain Expression Language (LCEL), helping you transition from basic API calls to sophisticated, multi-step orchestration. What you'll learn: - Understand the fundamental concept of Runnables and the LangChain execution lifecycle - Apply LangChain Expression Language syntax to pipe data smoothly between components - Configure custom prompts, language models, and structured output parsers to work as a unified pipeline - Manage state and pass variables dynamically through complex execution chains - Implement error handling and basic debugging patterns within your runnable workflows You will start by exploring core definitions and simple chain configurations before reading through practical implementations of multi-step AI workflows. Through clear written explanations and structured code analysis, you will gain a practical mental model of how data flows through modern orchestrators. This course is designed for beginner developers and AI enthusiasts who want to build structured AI applications. No prior experience with LangChain is required. Start reading today to master the core building blocks of modern AI orchestration.