The Complete LangChain & LLMs Guide

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Master the fundamentals and advanced capabilities of LangChain, LangGraph, and Large Language Models while building production-ready AI applications. You'll gain practical experience with prompt engineering, chains, agents, memory, embeddings, vector databases, Retrieval-Augmented Generation (RAG), and modern AI workflows. Through hands-on coding and real-world projects, you'll develop the confidence to design intelligent, scalable LLM-powered systems from the ground up. The course begins by helping you set up a professional Python development environment before introducing the foundations of LLMs and the LangChain ecosystem. You'll then explore prompt templates, output parsers, LangChain Expression Language (LCEL), runnable chains, memory management, document processing, embeddings, vector stores, and retrieval techniques. Each topic combines conceptual explanations with practical implementation to reinforce learning through experience. As you progress, you'll build increasingly sophisticated AI pipelines using RAG architectures, LangGraph workflows, conditional routing, human-in-the-loop systems, and multi-node agents. The course concludes with end-to-end projects, including a Smart Q&A Bot, an AI Research Assistant, and an image-to-text application with a Streamlit interface, giving you real-world experience developing intelligent applications. This course is ideal for Python developers, AI engineers, software developers, data professionals, and technology enthusiasts who want to build modern LLM applications. Learners should have basic Python programming knowledge and familiarity with APIs. The course is designed for an Intermediate audience seeking practical, industry-relevant AI development skills. By the end of the course, you will be able to build complete LangChain applications, develop RAG pipelines, implement LangGraph agents, manage conversational memory, integrate multiple LLM providers, optimize retrieval workflows, and deploy intelligent AI applications using modern best practices.