Building AI Applications with NIM and LangChain

Modern artificial intelligence development requires fast, optimized inference combined with flexible orchestration. Integrating NIM microservices with LangChain provides a robust foundation for building responsive generative AI pipelines, automated agents, and knowledge retrieval systems. Through clear written explanations and practical code snippets, this course guides you through foundational AI service integration step by step. What you'll learn: Understand key concepts and terminology behind inference microservices and LangChain architecture; Configure and connect NIM model endpoints to custom Python workflows; Create dynamic prompt templates and manage structured model outputs; Build Retrieval-Augmented Generation (RAG) pipelines using vector embeddings; Master prompt chaining and multi-step reasoning patterns for real-world tasks. The course begins with core definitions, API conventions, and fundamental concepts before progressing to complete multi-step applications. Designed for beginners and software developers, this text-only guide requires no prior AI engineering experience to get started. Begin reading today to expand your skills in modern AI application development.