How does RAG improve the quality of responses generated by LLMs?

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)

Why is RAG important for AI professionals?

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of AI models by grounding their responses in up-to-date, real-world information, which reduces the risk of incorrect or outdated outputs.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.

What’s the job outlook for professionals with RAG skills?

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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What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Build RAG Applications: Get Started

Build RAG Applications: Get Started

This course is part of multiple programs.

Instructors: Wojciech 'Victor' Fulmyk

24,450 already enrolled

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3 modules

Gain insight into a topic and learn the fundamentals.

152 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.

152 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

Tools you'll learn

Details to know

Shareable certificate

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Assessments

6 assignments

Taught in English

91%

of learners achieved a positive career outcome

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Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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!

Module details

This module provides an overview of Retrieval-Augmented Generation (RAG), illustrating how it can enhance information retrieval and summarization for AI applications. The module features a lab designed to introduce the fundamental components of building RAG applications, presented in an easy-to-use Jupyter Notebook format. Through this hands-on project, you’ll learn to split and embed documents and implement retrieval chains using LangChain.

What's included

4 videos2 readings2 assignments1 app item1 discussion prompt3 plugins

4 videosTotal 22 minutes

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

2 readingsTotal 6 minutes

Course Overview4 minutes
Summary and Highlights: Introduction to RAG2 minutes

2 assignmentsTotal 36 minutes

Graded Quiz: Introduction to RAG21 minutes
Practice Quiz: What is RAG? 15 minutes

1 app itemTotal 60 minutes

Summarize Private Documents Using RAG, LangChain, and LLMs60 minutes

1 discussion promptTotal 10 minutes

[Optional] Discussion Prompt: Meet and Greet10 minutes

3 pluginsTotal 20 minutes

Reading: Helpful Tips for Course Completion5 minutes
Reading: What is RAG?10 minutes
Cheat Sheet: Introduction to RAG 5 minutes

In this module, you'll learn to build a Retrieval-Augmented Generation (RAG) application using LangChain, gaining hands-on experience in transforming an idea into a fully functional AI solution. You'll also explore Gradio as a user-friendly interface layer for your models, setting up a simple Gradio interface to facilitate real-time interactions. Finally, you'll construct a QA Bot leveraging LangChain and an LLM to answer questions from loaded documents, reinforcing your understanding of end-to-end RAG workflows.

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

This module introduces you to LlamaIndex as an alternative to LangChain, helping you understand how to apply your RAG knowledge across different frameworks. You will explore the differences between these frameworks and gain hands-on experience by building a bot with IBM Granite and LlamaIndex that provides individuals with suggestions on engaging in conversations. When completing this project, you will learn about implementing key concepts such as vector databases, embedding models, document chunking, retrievers, and prompt templates to generate high-quality responses. 