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Fix for docs #39

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marcromeyn merged 1 commit into from
Dec 18, 2025
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Fix for docs #39

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6 changes: 3 additions & 3 deletions use-case-examples/README.md
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Expand Up @@ -6,13 +6,13 @@ This directory contains end-to-end examples demonstrating practical applications

## 📋 Overview

These examples showcase **complete implementations** of agentic workflows, RAG systems, and other advanced use cases. Unlike the [usage-cookbook](../usage-cookbook/) which focuses on deployment and basic model usage, these examples demonstrate how to build production-ready applications.
These examples showcase **complete implementations** of agentic workflows, RAG systems, and other advanced use cases. Unlike the {doc}`usage-cookbook </usage-cookbook/README>` which focuses on deployment and basic model usage, these examples demonstrate how to build production-ready applications.

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## Usage Examples Table of Contents

| Example | Description |
|---------|-------------|
| [RAG Agent with Nemotron RAG Models](./RAG%20Agent%20with%20Nemotron%20RAG%20Models/) | End-to-end example of a Retrieval-Augmented Generation (RAG) agent workflow using Nemotron RAG models through Hugging Face and Nemotron 9B hosted through build.nvidia.com models |
| [Data Science ML Agent](./Data%20Science%20ML%20Agent/) | End-to-end example of a natural language-driven data science and machine learning agent powered by NVIDIA GPUs. The agent allows users to perform data exploration, model training, and hyperparameter optimization interactively using RAPIDS cuDF and cuML for GPU acceleration.|
| {doc}`RAG Agent with Nemotron RAG Models </use-case-examples/RAG Agent with Nemotron RAG Models/README>` | End-to-end example of a Retrieval-Augmented Generation (RAG) agent workflow using Nemotron RAG models through Hugging Face and Nemotron 9B hosted through build.nvidia.com models |
| {doc}`Data Science ML Agent </use-case-examples/Data Science ML Agent/README>` | End-to-end example of a natural language-driven data science and machine learning agent powered by NVIDIA GPUs. The agent allows users to perform data exploration, model training, and hyperparameter optimization interactively using RAPIDS cuDF and cuML for GPU acceleration.|