Gen AI - RAG Application Development using LangChain
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Gen AI - RAG Application Development using LangChain
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What you'll learn
Gain expertise in LangChain and its various components to build AI applications
Learn to integrate vector databases and embeddings into language model applications
Master the concepts of Retrieval-Augmented Generation (RAG) for enhanced language processing
Develop conversational memory features to maintain context in multi-turn AI interactions
Skills you'll gain
Tools you'll learn
Details to know
February 2026
5 assignments
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There are 3 modules in this course
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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.
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.
What's included
8 videos1 reading1 assignment
8 videos•Total 157 minutes
- Introduction to the Course•13 minutes
- Introduction to Large Language Models•33 minutes
- Introduction to LangChain Framework•23 minutes
- Introduction to Prompts•25 minutes
- Environment Setup•18 minutes
- Installing Dependencies•18 minutes
- Using Google Gemini LLM (instead of OpenAI GPT)•5 minutes
- Code Demo - Simple ways of forming a Prompt and using it to Chain with a Model•21 minutes
1 reading•Total 10 minutes
- Full Course Resources•10 minutes
1 assignment•Total 15 minutes
- Introduction- Assessment•15 minutes
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.
What's included
10 videos1 assignment
10 videos•Total 240 minutes
- Getting Started with Prompt Template and Chat Prompt Template•23 minutes
- Working with Agents and Tools•42 minutes
- Agents and Tools - Advanced•19 minutes
- Document Loaders and Splitters•44 minutes
- Working with Output Parsers•17 minutes
- Language Embeddings and Vector Databases•41 minutes
- Our First RAG Application using a Vector DB•16 minutes
- Chain Types - Stuff, Map-Reduce and Refine•18 minutes
- LCEL - LangChain Expression Language•5 minutes
- Our First Langchain Program•13 minutes
1 assignment•Total 15 minutes
- LangChain Fundamental Concepts - Assessment•15 minutes
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.
What's included
8 videos3 assignments
8 videos•Total 234 minutes
- Working with SQL Data - RAG App•12 minutes
- RAG with Conversational Memory•22 minutes
- Create a CV Upload and CV Search Application•22 minutes
- Create a Website Query Conversational Chatbot - Project•50 minutes
- Analysis of Structured Data from a CSV/Excel using Natural Language•27 minutes
- Invoice Extraction RAG Application•24 minutes
- Traces and Evaluation with LangSmith•59 minutes
- Capstone Project•18 minutes
3 assignments•Total 90 minutes
- Full Course Practice Assessment•15 minutes
- RAG Applications and Projects - Assessment•15 minutes
- Full Course Assessment•60 minutes
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Frequently asked questions
This course, Gen AI - RAG Application Development using LangChain, focuses on building advanced AI applications by using the LangChain framework in combination with large language models (LLMs). It is particularly relevant as it teaches students how to integrate Retrieval-Augmented Generation (RAG) techniques, which are crucial for improving the performance and accuracy of AI models by incorporating external knowledge bases. This makes it an essential skill in developing intelligent systems capable of handling complex real-world tasks, such as document analysis, conversational chatbots, and data extraction.
This course offers an in-depth exploration of LangChain, a popular framework for developing applications with large language models. It covers foundational concepts, including prompt design, agent integration, working with vector databases, and the development of Retrieval-Augmented Generation (RAG) applications. The course also walks students through the practical steps of building applications using real-world examples, from SQL data queries to invoice extraction. By the end of the course, learners will have the knowledge to create complex AI applications powered by language models.
After completing this course, you will be able to develop and deploy RAG-based applications using the LangChain framework. You will gain hands-on experience in creating chatbots, document processing applications, and systems that interact with databases and external knowledge sources. Additionally, you will understand how to optimize prompts and integrate memory for more efficient AI-powered solutions, preparing you to tackle real-world problems with LangChain and large language models.
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