AI Application Development with LangChain and LangGraph

Modern AI development is moving beyond simple chat interfaces toward complex, multi-step workflows and autonomous agents. This course provides a structured path to mastering the tools that make these advanced applications possible. You will progress from understanding basic Large Language Model interactions to architecting complex systems that can reason, use tools, and maintain state across interactions. What you'll learn: - Understand the fundamental architecture of LangChain for connecting models to external data and tools - Master prompt engineering techniques to improve the reliability and accuracy of AI responses - Apply Retrieval-Augmented Generation (RAG) patterns to build applications that utilize specific private data - Design stateful, multi-agent workflows using LangGraph to handle complex logic and decision-making - Practice implementing memory and persistence so your applications can maintain context over time - Configure tool-calling capabilities to allow AI agents to interact with external APIs and services The course begins with foundational terminology and core concepts before guiding you through the logic of chaining operations and managing agentic state. You will read detailed explanations and study code snippets that illustrate how to build robust, production-ready AI systems. This course is designed for beginners and developers who are new to AI orchestration frameworks; no prior experience with LangChain or LangGraph is required. Start building the next generation of intelligent applications today.