The Complete LangChain & LLMs Guide

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

13 hours 42 minutes
Sessions

On-Demand
Subtitles

English

Found in

Part of

Langchain and Langgraph

Overview

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Updated in May 2025. This course now 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. Unlock the potential of LangChain and Large Language Models (LLMs) with this comprehensive course designed for AI enthusiasts and developers. From foundational concepts to building advanced applications, you’ll gain hands-on expertise in integrating cutting-edge AI tools to solve real-world challenges. Begin your journey by setting up your development environment, including Python and essential APIs, to create an optimized workspace. Dive deep into the core principles of LLMs and LangChain, exploring their components like chains, agents, memory, and parsers. Through engaging videos and hands-on exercises, you’ll learn to construct AI-driven solutions with precision and scalability. As the course progresses, you’ll tackle real-world projects, such as a newsletter generator, PDF data extractor, and multi-document chatbot. You’ll also explore creative implementations like an image-to-text recipe generator, gaining practical experience with tools like Streamlit, HuggingFace, and Chroma DB. Whether you're an AI enthusiast or a seasoned developer, this course offers valuable insights and practical skills. A basic understanding of Python is recommended, making this course suitable for learners at an intermediate level.

Syllabus

Introduction

In this module, we will provide an overview of the course, explaining what you will learn and the tools required. We will also showcase the exciting projects you will work on, so you can see how LangChain and LLMs will be applied in real-world scenarios. Finally, we’ll discuss how to connect with the instructor and peers for collaboration.

Development Environment Setup

In this module, we will guide you through setting up the essential tools needed for the course. This includes obtaining your OpenAI API key, installing Python, and configuring your Visual Studio Code environment with the necessary extensions. These steps are foundational for a smooth development process throughout the course.

LangChain and LLMs - Deep Dive

In this module, we will dive deep into LangChain and LLMs, exploring their roles in AI development. You will learn the essential components of LangChain, such as chains and agents, and understand their interactions. We will also investigate the different types of language models available within LangChain and their applications.

LangChain Prompts Template

In this module, we will introduce LangChain prompt templates and demonstrate their significance in improving AI interactions. You will gain practical experience by creating your own prompt templates, allowing for more dynamic and responsive AI communication within your projects.

LangChain Parsers

In this module, we will explore LangChain parsers, tools that are critical for processing and interpreting AI-generated responses. You’ll learn about output parsers and their practical applications, as well as the Pydantic parser, which helps structure and validate outputs in AI systems.

LangChain Memory and Chains

In this module, we will explore LangChain’s memory capabilities and the importance of maintaining context in AI conversations. You will work hands-on with different types of LangChain chains, including LLMChain and Sequential Chain, to build dynamic applications like a lullaby generator using Streamlit.

LangChain Routers, Document Loading and Document Splitting

In this module, we will examine LangChain routers, which enable the efficient distribution of tasks within an AI system. You will also learn how to load and split documents, making it easier to process large amounts of text. Hands-on exercises will help you become proficient with tools like CharacterTextSplitter and RecursiveCharacterTextSplitter.

LangChain Embeddings and Vectorstores

In this module, we will explore vector stores and embeddings, focusing on their role in AI’s semantic analysis of text. You will engage in hands-on exercises to test semantic similarity, as well as learn how to save embeddings to Chroma DB for efficient information retrieval and analysis.

LangChain Agents - Deep Dive

In this module, we will conduct a deep dive into LangChain agents, explaining their use in automating AI tasks. You will learn how to create tools for agents, explore various types of agents, and work hands-on with conversational agents and memory to improve their responsiveness and performance.

[REAL-WORLD] App - PDF Extractor

In this module, we will guide you through building the Bill Extractor app, which extracts data from PDFs. You will set up the core functionality, develop the front-end interface, and test the application to ensure it works smoothly, ready for use in real-world scenarios.

[REAL-WORLD] App - Newsletter Generator

In this module, you will develop a fully functional Newsletter Generator app. You’ll set up the search functionality, curate the best articles, and generate informative newsletters. By the end of the module, you will also create a user-friendly front-end interface using Streamlit.

[REAL-WORLD] App - Multi-document Chatbot

In this module, we will walk you through the creation of a multi-document chatbot that can analyze and answer questions about resumes. You will integrate LangChain’s QAChain for enhanced capabilities and learn how to deploy the chatbot with Streamlit for an engaging user interface.

[REAL-WORLD] App - Image to Text

In this module, we will explore the creation of an app that converts images into text and generates recipes. You will learn how to use HuggingFace for image captioning and implement text-to-speech functionality. Finally, you will integrate the features into a complete image-to-recipe application with Streamlit.

Next Steps

In this final module, we will recap everything you’ve learned in the course. We’ll also provide guidance on the next steps in your AI development journey, helping you stay on track and explore further learning opportunities with LangChain and LLMs.