Last Updated : 18 Apr 2025

Generative AI & RAG Systems on NVIDIA Course Overview

Unlock the future of technology with our Generative AI & RAG Systems on NVIDIA course. Spanning two days, this course provides an immersive dive into Generative AI and Retrieval-Augmented Generation (RAG) Systems. With a basic understanding of deep learning and intermediate Python skills, you'll learn to create new content using neural networks. In Module 01, we'll explore the foundations of Generative AI, its applications, and the challenges and opportunities it presents. Module 02 delves into building sophisticated RAG agents using Large Language Models (LLMs). By the end, you'll master dialog management, document interaction, and embedding models for content retrieval.

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Flexi Video	16,449
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+ GST 18%	4,259
Total Fees (without exam & Labs)	22,359 (INR)
Total Fees (with Labs)	28,359 (INR)

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Schedule personalized sessions based upon your availability.

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Learning without limits. Create custom courses that fit your exact needs, from blended topics to brand-new content.

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Experience exceptional training with the confidence of our Happiness Guarantee, ensuring your satisfaction or a full refund.

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Immerse yourself in a focused learning environment, free from distractions, where you can sharpen your skills in popular global destinations.

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Course Prerequisites

Minimum Required Prerequisites for the Generative AI & RAG Systems on NVIDIA Course

To ensure a successful learning experience in the Generative AI & RAG Systems on NVIDIA course, we recommend the following prerequisites:

Introductory Deep Learning Knowledge: A basic understanding of deep learning principles is necessary. Familiarity with frameworks such as PyTorch and an understanding of transfer learning is preferred.
Intermediate Python Experience: Proficiency in Python programming, including a good grasp of object-oriented programming and experience with Python libraries, is required.

These prerequisites will help you make the most out of the course and ensure you can effectively engage with the material presented. If you're comfortable with these topics, you'll be well-prepared to dive into the exciting world of Generative AI and Retrieval-Augmented Generation (RAG) Systems using NVIDIA technologies.

Target Audience for Generative AI & RAG Systems on NVIDIA

This course covers Generative AI and RAG Systems using NVIDIA, designed for professionals seeking to deepen their knowledge and practical skills in deploying advanced AI models.

AI Engineers
Data Scientists
Machine Learning Engineers
Deep Learning Practitioners
Software Developers
Research Scientists
IT Professionals specializing in AI
Academic Researchers in AI and Machine Learning
AI Enthusiasts with Python and deep learning experience
Tech Leads and Project Managers in AI and Data Science
Professionals aiming to upskill in Generative AI
Computational Linguists
Data Analysts with coding proficiency

Learning Objectives - What you will Learn in this Generative AI & RAG Systems on NVIDIA?

Introduction

The Generative AI & RAG Systems on NVIDIA course provides a comprehensive insight into Generative AI concepts and the deployment of retrieval-augmented generation systems using large language models. Over two days, participants will explore foundational knowledge, practical applications, and advanced orchestration techniques for building effective AI systems.

Learning Objectives and Outcomes

Module 01: Introduction to Generative AI

Define Generative AI and explain how Generative AI works
Describe various Generative AI applications
Explain the challenges and opportunities in Generative AI

Module 02: Building RAG Agents with LLMs

Compose an LLM system that can interact predictably with a user by leveraging internal and external reasoning components
Design a dialog management and document reasoning system that maintains state and coerces information into structured formats
Leverage embedding models for efficient similarity queries for content retrieval and dialog guardrailing
Implement, modularize, and evaluate a RAG agent that can answer questions about the research papers in its dataset without any fine-tuning
Explore LLM inference interfaces and microservices
Design LLM pipelines using LangChain, Gradio, and LangServe
Manage dialog states and integrate knowledge extraction
Develop strategies for working with long