NVIDIA AI Workbench Example Projects#
To enhance your experience with NVIDIA AI Workbench, we recommend starting with the AI Workbench Onboarding Project. This interactive guide is designed to familiarize you with the platform’s features through hands-on exercises, providing a solid foundation for your AI development journey.
Once you’ve completed the AI Workbench Onboarding Project, then you can explore our diverse collection of example projects tailored to various AI tasks. These projects serve as practical references and starting points for your own development.
Each project is accompanied by detailed documentation and can be easily cloned and opened within AI Workbench (Desktop App or CLI), streamlining your workflow and accelerating your development process.
If you are already familiar with AI Workbench, and want to learn how to use an example project, see Advanced Walkthrough: Hybrid RAG. If you are just getting started with AI Workbench, see Basic Quickstart.
The following example projects are available:
Example Projects for Retrieval Augmented Generation (RAG) - Implement advanced retrieval techniques to enhance generative models with multimodal document processing and enterprise-grade RAG pipelines.
Example Projects for Fine-Tuning Models - Customize pre-trained models including Llama, Mistral, and SDXL to suit specific applications and datasets using parameter-efficient techniques.
Example Projects for Data Science Workflows - Utilize RAPIDS and other tools for efficient data processing, analysis, and machine learning workflows.
Example Projects for Blueprint Workflows - Build enterprise-grade AI applications using NVIDIA Blueprints for PDF-to-podcast conversion, deep research assistants, and production-ready RAG solutions with NVIDIA NIM microservices.
Example Projects using NIM Deployments - Build and deploy custom NVIDIA NIM microservices for scalable AI model serving and inference.
Next Steps#
Learn the Concepts