AI Engineering with LangChain: Build Intelligent Applications

Large language models are transforming how we build software, but connecting them to real-world data and workflows requires a structured framework. This text-based course guides you through the core concepts of AI engineering using LangChain, the leading library for building context-aware applications. You will transition from writing simple prompts to designing robust AI systems. By reading clear explanations and analyzing practical code examples, you will understand how to orchestrate complex LLM workflows, manage application state, and connect models to external data sources. What you'll learn: Understand the foundational architecture of LangChain and the AI engineering lifecycle; Compose reusable prompts and manage context windows effectively; Implement Retrieval-Augmented Generation (RAG) to connect LLMs with vector databases; Build custom chains using LangChain Expression Language (LCEL) for reliable execution; Design autonomous agents and tools that can make decisions and execute actions; Apply modern evaluation techniques to monitor and improve your AI applications. The course starts with basic terminology and foundational concepts before moving step-by-step into data ingestion, memory management, and agentic workflows. You will learn by reading structured explanations and studying real-world code patterns. Designed for software developers, data professionals, and tech enthusiasts who are new to AI engineering, this course requires only basic familiarity with Python. Start reading today to build the foundations of your AI engineering career.