# Message Tagging

Automatically detect and respond to specific types of user messages using AI-powered semantic matching


Message tags are one of the most powerful features in Chipp. They let you automatically detect specific types of messages and take action—whether that's routing to a human, blocking harmful content, or tracking purchase intent.

## What Can You Do with Tags?

Tags unlock a wide range of automation possibilities. Here are just a few:

<TagUseCaseExplorer />

## How Tag Detection Works

Unlike simple keyword matching, Chipp uses **semantic similarity** to understand the meaning behind messages. This means your tags will catch variations and paraphrases you never explicitly defined.

<TagDetectionDemo />

### Why Semantic Matching Matters

Traditional keyword-based systems require you to anticipate every possible way someone might phrase a request. With semantic matching:

- "I want to talk to a human" matches "Can I speak with a real person?"
- "What's the price?" matches "How much does this cost?"
- "I'm frustrated" matches "This is really annoying"

You define a few example phrases, and the AI understands the underlying intent.

## Getting Started

Ready to set up your first tag? Follow this step-by-step guide:

<TagSetupSteps />

## Tag Configuration Options

Each tag has several settings you can customize:

### Tag Name
Give your tag a clear, descriptive name. You'll see this in your analytics and when reviewing chat history, so make it easy to understand at a glance.

**Good names:** "Human Escalation", "Pricing Question", "Complaint"
**Bad names:** "Tag 1", "Important", "Check"

### Example Phrases
Add 3-5 phrases that represent the type of message you want to detect. The AI uses these to understand the semantic meaning, so focus on variety:

```
Good examples for "Human Needed":
- "I need to talk to a human"
- "Can I speak with a real person?"
- "Connect me to support"
- "I want to talk to someone"

Less effective (too similar):
- "Talk to human"
- "Speak to human"
- "Human please"
- "Get me a human"
```

### Detection Sensitivity

The sensitivity slider controls how closely a message must match your examples to trigger the tag:

| Sensitivity | Best For | Trade-off |
|-------------|----------|-----------|
| Low (50-65%) | Catching all possible variations | More false positives |
| Medium (70-80%) | Balanced detection | Good starting point |
| High (85-95%) | Precise matching only | May miss variations |

**Tip:** Start at 75% and adjust based on what you see in your chat history.

### Blocking Response (Optional)

For safety or compliance tags, you can replace the AI's response entirely when a tag is detected. This is useful for:

- **Harmful content:** Block and provide a polite refusal
- **Off-topic requests:** Redirect to your app's purpose
- **Sensitive topics:** Provide specific guidance or disclaimers

When you add a blocking response, the AI won't generate anything—your exact text will be shown instead.

## Best Practices

### Start Small
Begin with 2-3 high-value tags rather than trying to cover every scenario. You can always add more as you learn what your users are asking.

### Review Matches Regularly
Check your tag matches in the chat history to spot false positives and missed detections. This helps you tune your sensitivity and add better example phrases.

### Use Specific Examples
The more specific and varied your example phrases, the better the detection. Generic phrases lead to more false positives.

### Don't Over-Block
Only use blocking responses when absolutely necessary. For most tags, it's better to let the AI respond naturally while you track the conversation.

## Common Tag Configurations

Here are some popular tag setups to get you started:

### Customer Support Bot

| Tag | Purpose | Blocking? |
|-----|---------|-----------|
| Human Needed | Route frustrated users to support | No |
| Refund Request | Flag for manual review | No |
| Positive Feedback | Track happy customers | No |

### Sales Assistant

| Tag | Purpose | Blocking? |
|-----|---------|-----------|
| Purchase Intent | Identify hot leads | No |
| Pricing Question | Track interest | No |
| Competitor Mention | Monitor positioning | No |

### Knowledge Base

| Tag | Purpose | Blocking? |
|-----|---------|-----------|
| Off-Topic | Redirect to purpose | Yes |
| Complex Question | Flag for follow-up | No |
| Feedback | Collect suggestions | No |

## Viewing Tag Analytics

Once your tags are active, you can see them in action:

1. **Chat History:** Each message shows any tags that were detected
2. **Tag Detail View:** Click on a tag to see all matches, sorted by recency
3. **Remove False Positives:** Click the X on any incorrectly tagged message to improve future detection

## Advanced Tips

### Combine with Custom Actions
Use tags as triggers for more complex workflows. When a tag is detected, you could:

- Send a notification to Slack
- Create a ticket in your CRM
- Update a spreadsheet
- Trigger an email

### Multiple Tags per Message
A single message can trigger multiple tags. For example, "I want a refund and need to speak to someone" might trigger both "Refund Request" and "Human Needed."

### Tag Categories
Organize related tags with consistent naming prefixes:
- `Support: Human Needed`
- `Support: Complaint`
- `Sales: Pricing Question`
- `Sales: Demo Request`

## Troubleshooting

### Tags Aren't Detecting Anything
- Lower your sensitivity threshold
- Add more varied example phrases
- Check that the tag is enabled

### Too Many False Positives
- Raise your sensitivity threshold
- Make example phrases more specific
- Remove overly generic examples

### Tags Detecting the Wrong Things
- Review your example phrases for unintended patterns
- Add negative examples (phrases that shouldn't match)
- Consider splitting into more specific tags

## Next Steps

Now that you understand message tagging, try setting up your first tag:

1. Think about what user messages you most want to detect
2. Create a tag with 3-5 good example phrases
3. Start at 75% sensitivity
4. Monitor your chat history for a few days
5. Tune based on what you observe

Tags become more valuable over time as you refine them based on real user interactions. Start simple, iterate often, and you'll build a powerful automation layer for your AI app.