Program Outline
Rapid Application Development Using Large Language Models
This NVIDIA DLI course gives teams applied knowledge of LLM application development by exploring the open-source ecosystem, including pretrained LLMs and frameworks that speed up solution delivery. It is well suited for organizations moving quickly into applied generative AI development.
Delivery
Virtual, On-site, or Hybrid
Duration
8 hours
Product
PyTorch, Hugging Face, LangChain, LlamaIndex
Role
AI Engineer, Developer
NVIDIA
Rapid Application DevelopmentPyTorch, Hugging Face, LangChain
NVIDIA LLM Apps
Best Fit
Audience Profile
Who This Program Is For
Built for developers who already understand core Python and basic deep learning concepts and now need a structured path into LLM application development.
Overview
Program Summary
Official NVIDIA DLI generative AI program focused on enterprise LLM application development using open-source models and frameworks.
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
Learn the LLM application stack
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Module 1
Learn the LLM application stack
Build working familiarity with pretrained large language models, open-source tooling, and the frameworks used to accelerate application development.
- LLM ecosystem foundations
- Working with pretrained open-source models
2Module 2
Prototype LLM solutions quickly
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Module 2
Prototype LLM solutions quickly
Use orchestration frameworks and applied development patterns to speed up application prototyping and implementation.
- Rapid application prototyping
- Framework-driven orchestration patterns
Coverage Areas
Topic Coverage
Coverage Item 1
LLM ecosystem foundations
Coverage Item 2
Working with pretrained open-source models
Coverage Item 3
Rapid application prototyping
Coverage Item 4
Framework-driven orchestration patterns
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 internal assistant or knowledge workflow as the scenario
- •Add retrieval, evaluation, or deployment emphasis
- •Extend into secure enterprise implementation and rollout planning
