# Response Quality Control

{% hint style="info" %}
[What is reply quality control? How to train it?](/tech/ai-agents/evaluation.md)
{% endhint %}

## How to View

Navigate to <mark style="color:blue;">"AI Features > AI Assistant > Reply Quality Control"</mark> in the left function menu to view recent conversation records and response content processed by the AI assistant, helping you understand the assistant's actual performance and usage.

If you need further analysis or data consolidation, you can also click the <mark style="color:blue;">blue "Export" button</mark> in the upper right corner. The system will automatically export the relevant conversation data as an Excel file for subsequent organization and use.

<figure><img src="/files/37tRDRp2OZyyL3iYzayE" alt=""><figcaption></figcaption></figure>

## Use Cases

### **Customer Service Quality Audit**

Analyze whether the assistant provides correct and clear responses to help continuously optimize reply content.

### **Training Materials**

Extract common questions and high-quality responses for use as internal training materials.

### **Reply Strategy Adjustment**

Adjust knowledge base content or role settings based on actual user questions and interaction behavior.

### **Performance Evaluation and Report Generation**

Support management in creating monthly or weekly reports to evaluate AI assistant usage effectiveness and coverage.

{% hint style="warning" %}
You can clarify the reasons for the following related issues:

* Whether sufficient data is provided
* Whether RAG found the data
* LLM cannot answer questions based on reference materials
  {% endhint %}


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# 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://docs.maiagent.ai/maiagent-user-guide/maiagent-user-guide-en/track/quality.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.