LangChain Foundations: Build Generative AI Workflows and Agents

Large language models are incredibly powerful, but to build truly intelligent applications, you need to connect them to external data, maintain conversation context, and allow them to take real-world actions. This course provides a clear, step-by-step pathway to mastering LangChain, the leading framework for orchestrating generative AI workflows. You will transition from writing simple prompts to designing sophisticated, multi-step chain architectures and autonomous agents. What you'll learn: - Understand the core architecture of LangChain and how it interacts with modern LLMs - Configure prompt templates to systematically guide model outputs and maintain consistency - Implement memory systems to help your applications retain context across multi-turn conversations - Build sequential and nested chains to execute complex, multi-step reasoning workflows - Connect models to external tools and APIs to create autonomous agents that solve real problems - Apply retrieval-augmented generation (RAG) concepts to ground model responses in custom data sources This course begins with foundational concepts, establishing key AI orchestration terminology before guiding you through environment setup and core components. You will read through practical implementation strategies and study clean, modern code snippets that demonstrate how to connect models, memory, and tools. Designed entirely for beginners, this text-only course requires no prior experience with AI frameworks, though a basic familiarity with Python is helpful. Start reading today to unlock the full potential of generative AI development.