> ## Documentation Index
> Fetch the complete documentation index at: https://docs.chatref.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Tags

> Organize and label your chats with automatic and manual tags, and learn how engagement types feed your Insights.

**Tags** are short labels that organize and describe your chats. They let you
scan the Inbox and instantly see which chats are about pricing, which are bug
reports, and which are just someone saying hello.

## Where tags come from

Tags arrive two ways, and you can mix both:

<CardGroup cols={2}>
  <Card title="Automatic" icon="robot">
    Your bot tags chats for you. Shortly after it replies, it reads the recent
    messages and applies any labels that fit – you don't have to lift a finger.
  </Card>

  <Card title="Manual" icon="hand-pointer">
    You create your own tags and apply them to chats yourself, for the categories
    that matter to your business.
  </Card>
</CardGroup>

## Engagement types

On top of your own tags, Chatref sorts **every** chat into one engagement type.
This is automatic and always on. It answers a simple question: did the visitor
actually ask for something?

| Type           | What it means                                                      | Example                              |
| -------------- | ------------------------------------------------------------------ | ------------------------------------ |
| **QUESTION**   | The visitor asked a real question or raised a problem.             | "Do you ship to Canada?"             |
| **NAVIGATION** | They just tapped a menu item or a quick option – no real question. | Tapping a **Track my order** button. |
| **NONE**       | A greeting, a thanks, or a test message.                           | "hi", "thanks!", "testing"           |

### Why it matters

Only **QUESTION** chats count in your Insights **answered vs couldn't answer**
rate. A coffee store owner doesn't want a hundred "hi" messages dragging down the
score – so greetings and menu taps are left out, and the rate reflects only the
chats where someone genuinely needed an answer.

<Card title="See your answered vs couldn't-answer rate" icon="chart-line" href="/insights/overview">
  Insights uses engagement types to show how often your bot actually answered.
</Card>

## Create a manual tag

<Steps>
  <Step title="Open the Tags tab">
    From your agent's dashboard, open the **Tags** tab.
  </Step>

  <Step title="Add a new tag">
    Click to create a tag, then give it a short, plain-English name – for example
    **Refund request** or **Booking question**.
  </Step>

  <Step title="Describe when it applies">
    Write a clear description of the kind of chat this tag is for. Your bot reads
    this description to decide which chats to label automatically.
  </Step>

  <Step title="Save">
    Save the tag. From now on, new chats that match are labeled for you.
  </Step>
</Steps>

## Re-tag past conversations

New tags apply to chats going forward. If you want a tag applied to older chats
too, you can re-tag your past conversations in one action from the Tags screen.

<Note>
  Tagging uses a small amount of your **Balance**, like other AI tasks – the bot
  reads the chat to label it. Tagging new chats as they arrive is light;
  re-tagging a batch of past conversations costs a bit more, because every older
  chat is read again.
</Note>

## Next steps

<CardGroup cols={2}>
  <Card title="Conversations" icon="inbox" href="/inbox/conversations">
    See where tags appear and how to read and reply to chats.
  </Card>

  <Card title="Insights" icon="chart-line" href="/insights/overview">
    Track your answered vs couldn't-answer rate, built from QUESTION chats.
  </Card>
</CardGroup>