Building RAG Agents with LLMs

About this course

This hands-on NVIDIA DLI course teaches you how to build intelligent agents using Large Language Models (LLMs) enhanced with Retrieval-Augmented Generation (RAG). You'll learn to create systems that combine the reasoning power of LLMs with real-time access to external data—a critical skill for building AI applications that actually work in the real world. NVIDIA's deep expertise in AI infrastructure makes this an authoritative guide to modern LLM application development.

What you'll learn

• How to design and implement RAG architecture that connects LLMs to knowledge bases and databases
• Vector embeddings fundamentals—converting text and data into forms LLMs can understand and retrieve efficiently
• Building and managing vector stores to organize and search your data at scale
• Using LangChain to orchestrate LLM workflows, chains, and agent behavior
• Practical techniques to reduce hallucinations and make LLM responses grounded in actual data
• How to structure multi-step reasoning tasks where agents can plan, act, and retrieve information dynamically
• Real-world deployment patterns for RAG systems in production environments

Who this is for

If you're building with LLMs or want to go beyond simple chatbots, this course is for you. You'll benefit most if you already understand Python and have basic familiarity with how LLMs work—but the course doesn't assume you're an expert.

• AI engineers and ML practitioners — level up from basic LLM usage to production-grade RAG systems that actually solve real problems
• Full-stack developers — add intelligent search and reasoning to your applications with proven frameworks and patterns

Prerequisites

You'll need working knowledge of Python, basic understanding of how Large Language Models work, and comfort with APIs. Familiarity with JSON and REST concepts is helpful. Prior experience with machine learning is not required.

Why this matters for Indian learners

RAG agents are transforming how Indian tech companies handle customer support, content discovery, and enterprise search. Whether you're building for a startup, a fintech platform, or an e-commerce business, RAG skills directly translate to roles in AI engineering and AI product development—fields where demand far exceeds supply in India's job market. Companies across Bangalore, Delhi, and Mumbai are actively hiring engineers who can build these systems.

Frequently asked questions

Is this course really free?

Yes. NVIDIA offers this as a completely free course—no hidden fees, no paywall for the certificate.

How long will it take to complete?

Plan for approximately 8 hours of hands-on work. Most learners complete it over 2–3 weeks by dedicating a few hours per week, though you can move faster if your schedule allows.

Will I get a certificate?

Yes. You'll earn a certificate of completion from NVIDIA DLI once you finish the course, which you can add to your resume and LinkedIn profile.