AI Engineering: Build LLM Applications with LangChain and LangGraph

The rise of large language models has opened up a new frontier for developers, but moving from simple prompts to production-ready AI applications requires a structured framework. This course provides a clear, text-based path to understanding how to orchestrate, debug, and deploy intelligent software. You will transition from a traditional developer to an AI engineer capable of building sophisticated, context-aware systems. By studying foundational concepts and analyzing practical code implementations, you will gain the confidence to construct autonomous agents and robust search architectures. What you'll learn: - Understand foundational AI engineering concepts, LLM APIs, and core orchestration terminology. - Build retrieval-augmented generation (RAG) systems to connect language models to external data sources. - Design multi-agent workflows and stateful applications using LangGraph. - Trace, debug, and monitor your AI application's performance with LangSmith. - Apply prompt engineering best practices and structure model inputs and outputs systematically. - Configure vector databases to store and retrieve semantic embeddings efficiently. The course begins with essential terminology and the basics of LLM APIs before moving systematically into chain construction, state management, and debugging. You will progress from simple text generation to multi-step agentic workflows through structured written explanations and code walkthroughs. This course is designed for software developers and tech enthusiasts who want to enter the AI space; no prior machine learning background is required, though basic programming familiarity is recommended. Start reading today to build the next generation of intelligent software.