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Build RAG Applications: Get Started

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IntermediateCourse

Explore retrieval-augmented generation (RAG) and its integration with generative models to enhance information retrieval. This course focuses on practical applications, guiding you through building RAG-based projects using LangChain and LlamaIndex. Gain a foundational to intermediate understanding through key concepts and hands-on examples.

Language

  • English

Topic

  • Artificial Intelligence

Industries

  • Information Technology

Skills You Will Learn

  • RAG, LangChain, Generative AI, Gradio, LlamaIndex

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 3 weeks

Platform

  • Coursera

Last Update

  • May 30, 2025
About this Course
Data scientists, AI researchers, robotics engineers, and others who can use retrieval-augmented generation (RAG) can expect to earn entry-level salaries ranging from USD 93,386 to USD 110,720 annually, with highly experienced AI engineers earning as much as USD 172,468 annually (Source: ZipRecruiter).
In this beginner-friendly short course, you’ll begin by exploring RAG fundamentals—learning how RAG enhances information retrieval and user interactions—before building your first RAG pipeline.
 Next, you’ll discover how to create user-friendly AI applications using Gradio, gaining experience with moving from project planning to constructing a QA bot that can answer questions using information contained in source documents.
Finally, you’ll learn about LlamaIndex, a popular framework for building RAG applications. Moreover, you’ll compare LlamaIndex with LangChain and develop a RAG application using LlamaIndex.  Throughout this course, you’ll engage in interactive labs and leverage multiple LLMs, gaining the skills needed to design, implement, and deploy AI-driven solutions that deliver meaningful, context-aware user experiences.

Enroll now to gain valuable RAG skills!

Course Learning Objectives 

  • Develop a practical understanding of Retrieval-Augmented Generation (RAG) 
  • Design user-friendly, interactive interfaces for RAG applications using Gradio 
  • Learn about LlamaIndex, its uses in building RAG applications, and how it contrasts with LangChain
  • Build  RAG applications using LangChain and LlamaIndex using Python
 

Course Syllabus

Module 1: Introduction to RAG
Lesson 0: Welcome to the Course
  • Video: Course Introduction
  • Reading: Course Overview
  • Reading: Helpful Tips for Course Completion
Lesson 1: What is RAG? 
  • Video: Why Rag?
  • Video: More RAG details
  • Reading: What is RAG?
  • Lab: Summarize private documents using RAG, LangChain, and LLMs
  • Practice Quiz: What is RAG?
Lesson 2: Module Summary and Evaluation
  • Reading: Summary and Highlights: Introduction to RAG
  • Reading: Cheatsheet: Introduction to RAG
  • Graded Quiz: Introduction to RAG
  • Discussion Prompt: Meet and Greet

Module 2: Build apps with RAG
Lesson 1: Create an Interactive RAG Application with a User-Friendly Gradio Interface
  • Video: Getting Started with Gradio
  • Reading: Introduction to Gradio 
  • Lab: Set Up a Simple Gradio Interface to Interact with Your Models
  • Lab: Construct a QA Bot That Leverages the LangChain and LLM to Answer Questions from Loaded Documents 
  • Practice Quiz: Building apps with RAG
Lesson 2: Module Summary and Evaluation
  • Reading: Summary and Highlights: Building apps with RAG
  • Reading: Cheatsheet: Building apps with RAG
  • Graded Quiz: Building apps with RAG

Module 3: Building RAG apps with LlamaIndex
Lesson 1: Application Development with LlamaIndex
  • Video: Intro to LlamaIndex: Document Ingestion and Chunking
  • Video: Intro to LlamaIndex: From Vector Stores to Query Engines
  •  Reading: LangChain vs LlamaIndex
  • Lab: Build an AI Icebreaker Bot with IBM Granite 3.0 & LlamaIndex
  • Practice Quiz: Application Development with LlamaIndex 
Lesson 2: Module Summary and Evaluation
  • Reading: Summary and Highlights: Build RAG Apps with LlamaIndex
  • Reading: Cheatsheet: Build RAG Apps with LlamaIndex
  • Graded Quiz: Build RAG Apps with LlamaIndex
Lesson 3: Course Wrap Up
  • Video: Course Wrap-up 
  • Reading: Congratulations and Next Steps
  • Reading: Team and Acknowledgments

Recommended Background

Python programming, familiarity with LangChain, and its use in developing simple generative AI applications 

Instructors

Wojciech "Victor" Fulmyk

Data Scientist at IBM

As a data scientist at the Ecosystems Skills Network at IBM and a Ph.D. candidate in Economics at the University of Calgary, I bring a wealth of experience in unraveling complex problems through the lens of data. What sets me apart is my ability to seamlessly merge technical expertise with effective communication, translating intricate data findings into actionable insights for stakeholders at all levels. Follow my projects to learn data science principles, machine learning algorithms, and artificial intelligence agent implementations.

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Hailey Quach

Data Scientist

Hi, I'm Hailey. I enjoy teaching others to build creative and impactful AI projects. By day, I’m a Data Scientist at IBM; by night, an Honors BSc student at Concordia University in Montreal, always exploring new ways to combine learning with innovation.

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