The Complete LangChain & LLMs Guide

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Overview

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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.

Syllabus

Introduction

In this module, we will begin your LangChain and LLM journey by introducing the course roadmap, learning objectives, and prerequisites required for success. You'll gain a clear understanding of what to expect throughout the course, set up for an effective learning experience, and discover how to connect with the instructor and community for ongoing guidance, support, and collaboration.

Development Environment Setup

In this module, we will prepare a complete development environment for building LangChain applications by configuring your OpenAI API key, installing Python, and setting up Visual Studio Code with the required extensions. By the end of this module, you'll have a fully functional workspace ready for developing, testing, and deploying AI-powered applications.

LangChain and LLMs - Deep Dive

In this module, we will build a strong conceptual foundation by exploring Large Language Models, the LangChain ecosystem, Version 1.0 architecture, and the core building blocks including components, chains, and agents. You'll also learn how to configure multiple LLM providers, enabling you to develop flexible and scalable AI applications.

LangChain Prompts Template

In this module, we will explore how prompt templates and message structures influence LLM behavior and response quality. Through detailed explanations and hands-on exercises, you'll learn to design reusable prompts that improve consistency, maintainability, and performance across AI applications.

LangChain Parsers

In this module, we will learn how to transform raw language model outputs into structured, reliable, and validated data using LangChain output parsers. You'll work with multiple parser types, model configurations, and Pydantic-based validation to build production-ready AI workflows.

LCEL - LangChain Expression Language

In this module, we will master LangChain Expression Language (LCEL) by learning how to build efficient, modular, and scalable AI pipelines. You'll explore runnable chains, streaming responses, schema inspection, branching logic, parallel execution, and debugging techniques to create production-grade workflows.

LangChain Memory

In this module, we will explore how memory enables language models to maintain context across conversations and workflows. You'll implement various memory strategies including windowed, summary, persistent, and multi-session memory to build more intelligent and context-aware AI applications.

Document Loading and Splitting

In this module, we will learn how to efficiently ingest, organize, and preprocess documents for LLM applications. You'll explore document loaders, chunking strategies, and specialized splitters that preserve semantic structure while optimizing retrieval performance.

Embeddings and Vector Stores

In this module, we will explore how embeddings and vector databases power semantic search and Retrieval-Augmented Generation (RAG) systems. You'll generate embeddings, optimize them through caching, configure Chroma vector stores, apply metadata filtering, and build efficient retrieval pipelines.

RAG - Retrieval Augmented Generation - New

In this module, we will build advanced Retrieval-Augmented Generation (RAG) systems by combining powerful retrieval techniques with modern language models. You'll implement basic and advanced RAG pipelines, structured outputs, hybrid search, contextual compression, parent document retrieval, and multi-query retrieval strategies to improve response quality and accuracy.

LangGraph Fundamentals - New

In this module, we will explore LangGraph, the next evolution of agentic AI development, by building graph-based workflows with nodes, edges, routing patterns, loops, and accumulated state. You'll also implement human-in-the-loop workflows that enable greater control, reliability, and scalability for advanced AI systems.

[Project] Smart Q&A Bot

In this module, we will apply everything you've learned to build a Smart Q&A Bot from scratch. You'll design the project architecture, implement the complete application, and integrate LangChain components, retrieval mechanisms, and conversational memory into a practical real-world solution.

[Project] AI Research Assistant

In this module, we will build a production-ready AI Research Assistant capable of ingesting information, retrieving relevant content, generating summaries, and producing structured responses. You'll combine advanced LangChain features with persistent memory to create an intelligent research companion.

[Real-World] App - Image to Text - Legacy

In this module, we will build a real-world multimodal AI application that transforms food images into recipes using image captioning and language models. You'll integrate Hugging Face models, add text-to-speech functionality, and develop an interactive Streamlit frontend to complete the end-to-end application.

Next Steps

In this module, we will conclude the course by exploring the next steps in your AI development journey. You'll discover advanced topics, recommended projects, and learning resources that will help you continue building expertise in LangChain, LLMs, and modern AI application development.