Overview

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. This comprehensive course will equip you with the skills to develop advanced language model applications using LangChain and Retrieval-Augmented Generation (RAG). Through hands-on projects and demonstrations, you will learn how to integrate large language models, prompt engineering, and vector databases into scalable AI-driven applications. Starting with the basics, the course progresses through fundamental concepts of LangChain and builds to complex RAG applications. The course begins by introducing core concepts such as LangChain, large language models, and the basics of prompts. It moves on to essential topics like agents, tools, and working with language embeddings, providing you with practical knowledge to construct powerful applications. You will then apply these skills to real-world projects, ranging from SQL data integration to building conversational chatbots and extracting information from invoices. With practical demonstrations and expert guidance, you will create sophisticated systems using LangChain and RAG techniques. By the end of the course, you will have developed hands-on projects that demonstrate your ability to build and deploy robust language model applications. You will gain proficiency in using advanced techniques like conversational memory, document parsing, and LangChain expression language, which are critical to modern AI applications. This course is designed for developers, data scientists, and AI enthusiasts eager to learn about language models and their real-world applications. Basic programming knowledge is required, and familiarity with Python will be beneficial. The difficulty level is intermediate, assuming the learner has some experience with AI concepts or software development. By the end of the course, you will be able to design and deploy Retrieval-Augmented Generation applications, utilize LangChain for AI application development, build and integrate vector databases, and optimize your applications using LangChain’s advanced tools.

Syllabus

Introduction

In this module, we will introduce the course objectives and key topics, including large language models, the LangChain framework, and prompts. You will learn how to set up your development environment, install dependencies, and gain practical insights into using Google Gemini LLM. Finally, you'll dive into hands-on coding with a simple prompt chaining demo to start building your own applications.

LangChain Fundamental Concepts

In this module, we will cover key LangChain concepts, starting with prompt templates and agents to advanced topics like document loaders, output parsers, and vector databases. You’ll also build your first Retrieval-Augmented Generation (RAG) application, work with different chain types, and learn the LangChain Expression Language (LCEL) for query construction. By the end of this module, you'll have a solid understanding of LangChain and the ability to write and execute your own LangChain programs.

RAG Applications and Projects

In this module, we will cover key LangChain concepts, including prompt templates, agents, and tools. You’ll explore language embeddings and vector databases, build a Retrieval-Augmented Generation (RAG) application, and learn to write your first LangChain program. By the end of this module, you'll have a comprehensive understanding of how to utilize LangChain for building advanced AI applications.