Retrieval-Augmented Generation (RAG) is rapidly becoming a core skill for Data Scientists, AI Engineers, and Software Developers, with competitive salaries reflecting its demand.

After completing this course, you will be able to:

Explain the core principles and benefits of Retrieval-Augmented Generation.
Describe how retrieval pipelines work, including chunking, embedding, and vector search.
Implement basic RAG workflows using Python and LangChain.
Design interactive user interfaces for RAG systems using Gradio.
Compare LlamaIndex and LangChain to determine their appropriate use cases.
Construct end-to-end RAG applications using LlamaIndex that answers questions from source documents.

In this course, you’ll start by learning how RAG improves information retrieval, context accuracy, and user interactions. You’ll build your first retrieval pipeline and experiment with document splitting, embedding, and retrieval workflows using Python.

You’ll design user-facing GenAI applications with Gradio, creating clean, interactive interfaces that connect your retrieval pipeline to real-time user queries. Through guided labs, you’ll transform project ideas into a working QA system capable of answering questions from loaded documents.

You’ll explore LlamaIndex as an alternative RAG framework, examining its structure, strengths, and differences compared with LangChain. By completing hands-on labs, you’ll build a full RAG application using both frameworks, gaining a practical understanding of when each tool is most effective.

By the end of this course, you’ll have the experience needed to design, implement, evaluate, and deploy end-to-end RAG applications that power context-aware AI solutions.

The following skills are required to be successful with this course:

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

Course Syllabus

Welcome to the Course

Course Introduction
Course Overview
RAG and Agentic AI Professional Certificate Overview
Reading: Helpful Tips for Course Completion

Module 1: Introduction to RAG

Module Summary and Learning Objectives
What Is RAG?
- Why RAG?
- More RAG Details
- Reading: What Is RAG?
- Summarize Private Documents Using RAG, LangChain, and LLMs
- Practice Quiz: What Is RAG?
Summary and Evaluation
- Summary and Highlights: Introduction to RAG
- Cheat Sheet: Introduction to RAG
- Graded Quiz: Introduction to RAG
- [Optional] Discussion Prompt: Meet and Greet

Module 2: Build Apps with RAG

Module Summary and Learning Objectives
Create an Interactive RAG Application with a User-Friendly Gradio Interface
Getting Started with Gradio
- Reading: Introduction to Gradio
- Lab: Set Up a Simple Gradio Interface to Interact with your Model
- Lab: Construct a QA Bot that Leverages the LangChain and LLM to Answer Questions from Loaded Documents
- Practice Quiz: Building Apps with RAG
Summary and Evaluation
- Summary and Highlights: Building Apps with RAG
- Cheat Sheet: Building Apps with RAG
- Graded Quiz: Building Apps with RAG

Module 3: Build RAG Apps with LlamaIndex

Module Summary and Learning Objectives
Application Development with LlamaIndex
- Intro to LlamaIndex: Document Ingestion and Chunking
- Intro to LlamaIndex: From Vector Stores to Query Engines
- Reading: LangChain and LlamaIndex Compared
- Lab: Build an AI Icebreaker Bot with IBM Granite 3.0 & LlamaIndex
- Practice Quiz: Application Development with LlamaIndex
Summary and Evaluation
- Summary and Highlights: Build RAG Apps with LlamaIndex
- Cheat Sheet: Build RAG Apps with LlamaIndex
- Graded Quiz: Build RAG Apps with LlamaIndex

Course Wrap Up

Course Wrap-Up
Congratulations and Next Steps
Team and Acknowledgments

RAG: Build Apps with LangChain and LlamaIndex

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