Course data sheet
NVIDIA Building RAG Agents with LLMs
H45WQS
Table of Contents
Overview
This course teaches you how to deploy a large language model (LLM) agent system in practice and scale up your system to meet the demands of users and customers. Along the way, you learn advanced LLM orchestration techniques for internal reasoning, dialog management, tooling, and retrieval.
This course takes a deep dive into the world of large language model (LLM) inference interfaces and the strategic use of microservices. You explore the design of LLM pipelines, and how to create dynamic and efficient systems using tools like LangChain, Gradio, and LangServe. You also learn how to manage dialog states, integrate knowledge extraction techniques, and employ strategies for handling long-form documents. This course also covers embeddings for semantic similarity and guardrailing, and how to implement vector stores for document retrieval.
Audience
This course is ideal for AI practitioners like developers, data scientists, AI engineers, and technical artists, who need to execute language-related tasks daily, such as text classification, content generation, sentiment analysis, and customer chat support, and they seek to do so in the most cost-effective way.
Prerequisites
Before attending this course, you should have Introductory deep learning knowledge, intermediate Python experience (including object-oriented programming and libraries), and understand the fundamentals of working with PyTorch.
Objectives
After completing this course, you should be able to:
- 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
- Use embedding models for efficient similarity queries for content retrieval and dialog guardrailing
- Implement, modularize, and evaluate a RAG agent that can answer questions about research papers in its dataset without any fine-tuning
- Understand RAG agents and the tools necessary to develop own LLM applications
Certifications and related exams
Upon successful completion of this course, students receive an NVIDIA DLI certificate of competency.
Course outline
| Module 1: LLM Inference Interfaces |
| Module 2: Pipeline Design with LangChain, Gradio, and LangServe |
| Module 3: Dialog Management with Running States |
| Module 4: Working with Documents |
| Module 5: Embeddings for Semantic Similarity and Guardrailing |
| Module 6: Vector Stores for RAG Agents |
| Module 7: Evaluation, Assessment, and Q&A |
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a50014575enw, H45WQS A.00, December 2025