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Build RAG Applications: Get Started

Build RAG Applications: Get Started

This course is part of multiple programs.

Instructors: Wojciech 'Victor' Fulmyk +1 more

28,170 already enrolled

Included with

3 modules

Gain insight into a topic and learn the fundamentals.

161 reviews

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

161 reviews

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

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 in Python

Skills you'll gain

Category: LLM Application
Category: Large Language Modeling
Category: Retrieval-Augmented Generation

Tools you'll learn

Category: AI Workflows

Details to know

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Assessments

6 assignments

Taught in English

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Develop job-relevant skills with hands-on projects
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There are 3 modules in 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 Generative AI applications using Python and 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 hands-on 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!

In this module, you will examine the purpose and core principles of Retrieval-Augmented Generation (RAG) and how its components work together to support information retrieval. You will also explore the structure of a basic RAG workflow for summarizing documents, handling conversational context, and responding to user queries.

What's included

4 videos4 readings2 assignments1 app item1 plugin

4 videosTotal 22 minutes

Course Introduction3 minutes
RAG and Agentic AI Professional Certificate Overview6 minutes
Why RAG?7 minutes
More RAG Details7 minutes

4 readingsTotal 26 minutes

Course Overview4 minutes
What is RAG?15 minutes
Summary and Highlights: Introduction to RAG2 minutes
Cheat Sheet: Introduction to RAG 5 minutes

2 assignmentsTotal 45 minutes

Graded Quiz: Introduction to RAG30 minutes
Practice Quiz: What is RAG? 15 minutes

1 app itemTotal 45 minutes

Summarize Private Documents Using RAG, LangChain, and LLMs45 minutes

1 pluginTotal 5 minutes

Reading: Helpful Tips for Course Completion5 minutes

In this module, you will explore how Retrieval-Augmented Generation (RAG) supports AI applications by combining language models with retrieval pipelines. You will consider the core components of a RAG system, how Gradio can support user interaction, and how an LLM, retrieval pipeline, and interface work together in a basic RAG application.

What's included

1 video1 reading2 assignments2 app items2 plugins

1 videoTotal 4 minutes

Getting Started with Gradio 4 minutes

1 readingTotal 2 minutes

Summary and Highlights: Building Apps with RAG 2 minutes

2 assignmentsTotal 36 minutes

Graded Quiz: Building Apps with RAG 21 minutes
Practice Quiz: Building Apps with RAG 15 minutes

2 app itemsTotal 60 minutes

Lab: Set Up a Simple Gradio Interface to Interact with Your Models30 minutes
Lab: Construct a QA Bot that Leverages the LangChain and LLM to Answer Questions from Loaded Document30 minutes

2 pluginsTotal 20 minutes

Reading: Introduction to Gradio 15 minutes
Cheat Sheet: Building Apps with RAG5 minutes

In this module, you will examine LlamaIndex as a framework for building RAG applications and how it differs from LangChain. You will focus on the core concepts used in a LlamaIndex-based RAG pipeline, including embeddings, vector databases, document chunking, retrievers, and prompt templates. You will also see how RAG knowledge transfers across frameworks while applying these concepts in a conversational app context.

What's included

3 videos3 readings2 assignments1 app item2 plugins

3 videosTotal 18 minutes

Intro to LlamaIndex: Document Ingestion and Chunking7 minutes
Intro to LlamaIndex: From Vector Stores to Query Engines6 minutes
Course Wrap-Up 5 minutes

3 readingsTotal 12 minutes

Summary and Highlights: Build RAG Apps with LlamaIndex5 minutes
Congratulations and Next Steps2 minutes
Team and Acknowledgments5 minutes

2 assignmentsTotal 31 minutes

Graded Quiz: Build RAG Apps with LlamaIndex 21 minutes
Practice Quiz: Application Development with LlamaIndex 10 minutes

1 app itemTotal 45 minutes

Lab: Build an AI Icebreaker Bot with IBM Granite 3.0 & LlamaIndex45 minutes

2 pluginsTotal 20 minutes

Reading: LangChain and LlamaIndex Compared15 minutes
Cheat Sheet: Build RAG Apps with LlamaIndex5 minutes

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Instructors

Instructor ratings

(32 ratings)

Wojciech 'Victor' Fulmyk

IBM

9 Courses139,734 learners

Offered by

IBM

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Showing 3 of 161

Reviewed on Aug 23, 2025

Despite being well structured course material and passing relevant experinece, the code showcased, the libraries used are outdated.

Reviewed on Jul 2, 2026

Good but the lab was out of date, the dependencies and LLM calling didnt work

Reviewed on Aug 30, 2025

The course is awesome!. I got clear understanding of RAG and LlamaIndex

Frequently asked questions

RAG improves the quality of responses generated by LLMs by grounding answers in up-to-date, authoritative external data to reduce errors and hallucinations. It enables LLMs to provide more accurate, context-aware, and reliable outputs, often with source citations, even for topics outside their original training data, which results in higher trustworthiness and relevance in AI-generated responses. (Source: GoPractice.io)

Retrieval-augmented generation (RAG) is important for AI professionals because it improves the accuracy and reliability of AI models by grounding their responses in up-to-date, real-world information, which reduces the risk of incorrect or outdated outputs. RAG also enables faster adaptation to new domains without extensive retraining, making AI solutions more flexible and cost-effective.

For AI professionals, mastering RAG means building more transparent, context-aware, and dependable AI systems, making the ability to implement RAG an essential skill as demand for trustworthy and explainable AI continues to grow across industries.

The job outlook for professionals with RAG (Retrieval-Augmented Generation) skills is highly promising, with demand rapidly increasing as industries like healthcare, finance, legal, and customer service adopt RAG. With the RAG market projected to grow at over 49.2% CAGR through 2034, professionals with these skills can expect strong job opportunities, competitive salaries, and career growth across multiple sectors. (Source: Precedence Research)

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