# Introduction to AI Applications

An AI application represents a high-level logical grouping of all Kubernetes resources (workloads, services, ConfigMaps, etc.) that together deliver a functional AI solution, such as a Retrieval-Augmented Generation (RAG) system or an image classification service.

## Key Characteristics

* **Unified purpose** - All grouped resources work together to deliver a single, identifiable functional goal.
* **Resource aggregation** - The AI application groups various NVIDIA Run:ai workloads and standard Kubernetes resources, providing a holistic view and management layer for complex, multi-component AI systems.
* **Simplified orchestration and observability** - Grouping related components into an AI Application makes it easier to identify resource dependencies and monitor the health and performance of the entire functional unit. This can help streamline operational tasks such as troubleshooting or scaling.
* **Collaboration and organization** - AI applications create a clear organizational boundary and naming structure for different functional AI systems running on the platform, aiding in organization and team collaboration.

## Deploying an AI Application with Helm

AI applications are commonly deployed using Kubernetes Helm charts into an NVIDIA Run:ai project or namespace. For guidance on creating or configuring Helm charts (templates, values, labels, etc.), use the official [Helm](https://helm.sh/docs/intro/using_helm/) documentation.

NVIDIA Run:ai does not manage Helm deployments directly. Instead, it assumes that:

* A functional Helm chart is deployed externally, and
* The resulting Kubernetes resources are created in a NVIDIA Run:ai project.

Once the chart is deployed:

* **Workload discovery** - Workloads created by the chart are automatically detected by NVIDIA Run:ai and appear in the [Workloads](https://run-ai-docs.nvidia.com/self-hosted/workloads-in-nvidia-run-ai/workloads) table.
* **Scheduling and management** - These workloads are scheduled, managed, and monitored according to NVIDIA Run:ai policies and capabilities. See [Supported features](https://run-ai-docs.nvidia.com/self-hosted/workloads-in-nvidia-run-ai/workload-types/supported-features#externally-submitted-kubernetes-workloads) for more details.
* **Application grouping** - NVIDIA Run:ai identifies related workloads and resources and groups them into a single AI application based on their shared context.
* **Aggregated visibility** - Resource consumption, status, and health are aggregated and presented at the AI application level, allowing the system to be viewed and analyzed as a single cohesive entity. See [AI applications](https://run-ai-docs.nvidia.com/self-hosted/ai-applications/ai-applications) for more details.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://run-ai-docs.nvidia.com/self-hosted/ai-applications/introduction-to-ai-applications.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.