Build RAG Applications: Get Started

Overview

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!

Syllabus

Introduction to RAG

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.

Build Apps with RAG

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.

Build RAG Apps with LlamaIndex

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.