Program Outline
Building RAG Agents With LLMs
This NVIDIA DLI course teaches teams how to design retrieval-augmented generation systems and bundle them into deliverable formats. It also explores advanced LLM composition techniques for internal reasoning, dialog management, and tooling.
Delivery
Virtual, On-site, or Hybrid
Duration
8 hours
Product
LangChain, FAISS, NVIDIA AI Foundation endpoints
Role
AI Engineer
NVIDIA
Agentic LLM SystemsGrounding, tools, orchestration
NVIDIA RAG Agents
Best Fit
Audience Profile
Who This Program Is For
Built for practitioners who want to move from general LLM usage into retrieval-grounded and agentic system design for production-facing solutions.
Overview
Program Summary
Official NVIDIA DLI generative AI program focused on retrieval-augmented generation systems and agent-based LLM workflows.
Course Outline
Complete Module Sequence
Review the full module sequence for this program, including the primary topic coverage in each module where available.
1Module 1
Design retrieval-grounded LLM systems
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Module 1
Design retrieval-grounded LLM systems
Learn the foundations of retrieval-augmented generation systems and the decisions involved in building grounded, useful LLM solutions.
- RAG system fundamentals
- Dialog and reasoning patterns
2Module 2
Build agentic and tool-using workflows
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Module 2
Build agentic and tool-using workflows
Use orchestration and packaging patterns to create more capable LLM agents that can interact with tools and deliver measurable outcomes.
- Agent and tool orchestration
- Deliverable system packaging
Coverage Areas
Topic Coverage
Coverage Item 1
RAG system fundamentals
Coverage Item 2
Agent and tool orchestration
Coverage Item 3
Dialog and reasoning patterns
Coverage Item 4
Deliverable system packaging
Customization
Adapt This Program for Your Team
We can adapt this program around your team structure, platform priorities, delivery goals, and the scenarios your people need to work through in practice.
- •Use your enterprise knowledge or document workflow as the basis
- •Add governance and evaluation emphasis for regulated scenarios
- •Extend into deployment, serving, and operationalization decisions
