## **Overview**

LLM Visibility tracks two distinct types of queries: **auto-generated queries** and **deployed queries**. Understanding the difference is essential to managing your monthly query quota and troubleshooting issues related to Atlas Brain deployment. If a deployment attempt fails or consumes more quota than expected, this article explains what to check and how to escalate.

## **What Are Auto-Generated Queries?**

Auto-generated queries are created automatically by the platform when you set up a project or topic in LLM Visibility. The system produces a set of relevant questions and prompts representing how real users might ask about your brand, product, or topic across large language models.

- Auto-generated queries are created in the background without manual input.
- They appear in your query list inside AI Visibility → Topics & Queries.
- Having auto-generated queries does not mean they have been deployed or tested against LLMs yet.

## **What Are Deployed Queries?**

Deployed queries are queries that have been actively sent to one or more LLMs for processing. Deployment is the action that triggers live data collection — it is when a query moves from a waiting state to an active, results-producing state.

- Deployment can be initiated manually or automatically by the platform.
- A query can only be deployed if your remaining quota can accommodate it.

## **How to Check Your Current Quota Usage**

To understand how your quota is being consumed, navigate to your LLM Visibility project and review the query list. You can distinguish between query types by checking the status column or labels associated with each query — auto-generated queries will typically show a pending or draft status until they are deployed. Review the total count of auto-generated queries versus those marked as deployed or active to get a sense of how your quota is being allocated. If your account dashboard shows a quota or usage indicator, compare your current consumption against your monthly limit to determine how much headroom remains before initiating a large deployment.

## **Steps to Take If Your Deployment Is Blocked**

If an Atlas Brain deployment appears blocked or does not complete as expected, try the following steps before escalating:

1. Review your current query list in LLM Visibility and count how many queries are in a deployed or active state versus auto-generated or pending.
2. Check whether your total active query count is approaching or at your plan's monthly limit, as this is the most common reason a deployment does not proceed.
3. If you have queries in an auto-generated state that you do not need, remove or archive them to free up quota before reattempting deployment.
4. Retry the deployment after making any adjustments and monitor whether it proceeds to an active state.

If the deployment remains blocked after these steps, our support team will need to review your account directly. Please have the following ready when you reach out:

- Your project name and the topic(s) affected
- The exact error message you received (if any)
- The approximate date and time the deployment was attempted
- The number of queries you were attempting to deploy

If you need further assistance, open the chat widget in the bottom-right corner of the platform and type **human teammate** to be connected with a member of our team.