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Mastering Generative AI: Agents with RAG and LangChain

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IntermediateCourse

Build job-ready skills for AI agents in just 2 weeks. Plus, valuable practical experience and a credential. Familiarity with RAG and LangChain.

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 1.00K

Skills You Will Learn

  • RAG, In-COntent Learning, Chatbots, Vector Databases, LangChain

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 2 Weeks 4 hours

Platform

  • edX

Last Update

  • June 8, 2025
About this Course
Hello and welcome to this course! 

In this course, you will explore how retrieval-augmented generation (RAG) generates a response when the model is not pre-trained. You will learn about the RAG process, including context and question encoders with their tokenizers, as well as the Facebook AI similarity search (Faiss) library. Additionally, you will gain insight into in-context learning, advanced methods of prompt engineering and its key elements, and prompt templates with LangChain. You will deep dive into the LangChain tools, components, chat models, and document loader. Finally, you will clearly understand how LangChain chains and agents are used to develop applications. 

In hands-on labs, you will use the Jupyter Labs environment to practice these concepts and technologies to build a solid foundation for their application in your projects. At the end of this course, you’ll also complete a project based on a real-world scenario. 

This course is part of the Generative AI Engineering Professional Certificate, designed to equip learners with the essential skills in Python, PyTorch, and neural networks in generative AI engineering with LLMs.  



Course Syllabus

Module 1: RAG Framework 
  • Reading: Module Introduction and Learning Objectives 
  • Video: RAG 
  • Video: RAG, Encoders, and FAISS 
  • Lab: RAG with Hugging Face 
  • Lab: RAG with PyTorch 
  • Practice Quiz: Module 1: RAG Framework 
  • Reading: Summary and Highlights  
  • Graded Quiz: RAG Framework
Module 2: Prompt Engineering and LangChain 
  • Reading: Module Introduction and Learning Objectives 
  • Video: Introduction to LangChain 
  • Video: Introduction to In-context Learning 
  • Video: Advanced Methods of Prompt Engineering 
  • Lab: In-Context Engineering and Prompt Templates 
  • Video: LangChain: Core Concepts 
  • Video: LangChain Documents for Building RAG Applications 
  • Video: LangChain Chains and Agents for Building Applications 
  • Lab: LangChain 
  • Lab: Guided Project: Summarize Private Documents Using RAG, LangChain, and LLMs 
  • Practice Quiz: Prompt Engineering and LangChain 
  • Reading: Summary and Highlights: Prompt Engineering and LangChain 
  • Graded Quiz: Prompt Engineering and LangChain 
  • Reading: Cheat Sheet: Fundamentals of Building AI Agents using RAG and LangChain 
  • Reading: Glossary: Fundamentals of Building AI Agents using RAG and LangChain 

Recommended Skills Prior to Taking this Course

Basic knowledge of generative AI, prompt engineering techniques, and working knowledge of machine learning with Python and PyTorch.  

Instructors

IBM Skills Network

IBM Skills Network Team

At IBM Skills Network, we know how crucial it is for businesses, professionals, and students to build hands-on, job-ready skills quickly to stay competitive. Our courses are designed by experts who work at the forefront of technological innovation. With years of experience in fields like AI, software development, cybersecurity, data science, business management, and more, our instructors bring real-world insights and practical, hands-on learning to every module. Whether you're upskilling yourself or your team, we will equip you with the practical experience and future focused technical and business knowledge you need to succeed in today’s ever-evolving world.

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