IBM

Build RAG Applications: Get Started

This course is part of multiple programs.

Wojciech 'Victor' Fulmyk
IBM Skills Network Team

Instructors: Wojciech 'Victor' Fulmyk

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26,304 already enrolled

Gain insight into a topic and learn the fundamentals.

157 reviews

Intermediate level

Recommended experience

7 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.

157 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

Details to know

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Assessments

6 assignments

Taught in English

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There are 3 modules in this course

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

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

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

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Instructors

Instructor ratings
(32 ratings)
Wojciech 'Victor' Fulmyk
IBM
9 Courses135,186 learners

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IBM

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