> For the complete documentation index, see [llms.txt](https://docs.zenml.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zenml.io/pro/core-concepts.md).

# Core Concepts

- [Hierarchy](https://docs.zenml.io/pro/core-concepts/hierarchy.md): Understanding ZenML's hierarchical structure
- [Organizations](https://docs.zenml.io/pro/core-concepts/organization.md): Manage organizations in ZenML
- [Workspaces](https://docs.zenml.io/pro/core-concepts/workspaces.md): Learn how to use workspaces in ZenML Pro.
- [Projects](https://docs.zenml.io/pro/core-concepts/projects.md): Managing projects in ZenML
- [Teams](https://docs.zenml.io/pro/core-concepts/teams.md): Learn about Teams in ZenML Pro and how they can be used to manage groups of users across your organization and workspaces.
- [Snapshots](https://docs.zenml.io/pro/core-concepts/snapshots.md): Trigger pipelines from the dashboard, SDK, CLI, or REST API.
- [Triggers](https://docs.zenml.io/pro/core-concepts/triggers.md): Trigger pipelines by schedule or event.
- [Resource Pools](https://docs.zenml.io/pro/core-concepts/resource-pools.md): Fair GPU and compute sharing for AI/ML teams: dependable production capacity, shared pools, idle reuse, and workspace-level quotas.
- [Core Concepts](https://docs.zenml.io/pro/core-concepts/resource-pools/resource-pools-core-concepts.md): Precise definitions for ZenML Pro resource pools, subject policies, and resource requests.
- [Reconciliation Process](https://docs.zenml.io/pro/core-concepts/resource-pools/resource-pools-reconciliation.md): How the resource pool reconciliation process works in ZenML Pro.
- [Examples](https://docs.zenml.io/pro/core-concepts/resource-pools/resource-pools-examples.md): Step-by-step ZenML Pro resource pool examples: pool JSON, policy JSON, ResourceSettings, and outcomes for new users.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## 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, and the optional `goal` query parameter:

```
GET https://docs.zenml.io/pro/core-concepts.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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.