AI Chat labeling

Overview

The AI Chat Labeling feature allows you to automatically extract crucial metrics and insights from your chat conversations using Artificial Intelligence. By configuring metrics, you can send chat transcript and the prompt for AI to analyze the conversation and extract the correct value (e.g., a classification, a label, a number, or a piece of text) based on your specific instructions.

This article covers:
Key Concepts and Components
Creating and Configuring an AI Chat Labeling Metric
Viewing Results in the Reporting Dashboard
Data Source: API Integration

Key Concepts and Components

Metrics

A metric is a specific piece of information to be extracted or calculated from a chat. Each metric is configured with a type, a data source, and specific instructions for the AI.

AI Labeling Credits

Processing a chat transcript with the AI consumes AI Labeling Credits. We've tied the use of Credits to the number of Chat conversations and number of metrics. 

All customers receive a default monthly allocation of credits. One credit is consumed for each chat and 5 metrics.  For customers with high chat volume, the default allocation may not cover all chats.
Additional credits can be purchased to ensure comprehensive labeling across all conversations. Please send your request for more credits to support@giosg.com

Data Sources

Metrics can pull data from two sources:

Data Source	Description
AI (Default)	The chat log is sent to the AI, and the value is determined based on the metric's configuration (prompt/description).
API	The metric value is provided by an external system or interaction via an API integration. This is ideal for importing values like an NPS score from a third-party survey tool.

Metric Types

The system supports five distinct metric types, determining the format of the output the AI (or API) provides:

Metric Type	Description	AI Output Constraint	Example Use Case
Classification	The AI must select exactly one value from a predefined list of classes.	Single Choice	Determine the intent of the chat (e.g., Sales, Support, General Inquiry).
Labeling	The AI can select zero, one, or multiple relevant values from a predefined list of labels.	Multi-Choice	Identify all products discussed (e.g., 'Product A', 'Product B', 'Service C').
Tagging	The AI can freely choose zero, one, or multiple tags that it deems relevant to the conversation.	Freeform Multi-Choice	Capture sentiment or key themes (e.g., 'Frustrated', 'Positive Feedback', 'Pricing Question').
Number	The AI provides a single numeric value.	Numeric Value	Extract the estimated deal size in a sales chat or a rating score.
Text	The AI provides a textual value.	Freeform Text	Summarize the key action taken or a required follow-up.

 

Creating and Configuring an AI Chat Labeling Metric

AI Chat Labeling configuration can be accessed by going to Giosg reporting dashboard at https://reporting.giosg.com and clicking “Reporting settings” from the three-dot menu in the top right corner of the page.

You can for example analyse the sentiment of the visitors in your chat conversations (see image below). This helps you understand where your visitors are experiencing negative vs. positive feelings, allowing you to identify potential room for improvement in the service you provide.

To create a new AI Labeling metric, you will need to provide the following information:
- General Settings, like name and metric type.
- Description for AI, meaning the prompt for how data is analysed-
- Metric-Specific Configurations, like how data should be classified.
- Excluded rooms, if you want to limit the data that will be analysed.
You'll find more information on these when you continue reading.  

1. General Settings

Field	Requirement	Description
Name	Required	A human-readable name for the metric (e.g., "Chat Intent Classification", "Customer Sentiment").
Metric Type	Required	Select one of the five types: Classification, Labeling, Tagging, Number, or Text.
Data Source	Required	Choose AI (Default) or API.

 

2. Description for AI (The Prompt)

This section is crucial as it instructs the AI on exactly what to do with the chat log.

Field	Requirement	Description
Name (for AI)	Required	A short, descriptive name the AI uses internally. It should help the AI understand its core task (e.g., "user intent", "mentioned products").
Description	Required	This is a description of the metric’s value, which is used as a part of the prompt sent to the AI. Clearly explain what is the expected value in each situation. You can also explain any special rules, contextual information, and the required output format.

Example Description Tip: "Visitor’s primary intent. If the intent is unclear, the value 'General Inquiry' should be the default value. If the visitor has multiple intents, the value should be the most important one."

 

3. Metric-Specific Configuration

Depending on your selected Metric Type, additional fields will be required to constrain the AI's output:

Metric Type	Required Field	Description
Classification	Classification Classes	A list of predefined values. The AI must select exactly one from this list.
Labeling	List of Labels	A list of predefined values. The AI can select zero, one, or multiple values from this list.

 

4. Excluded rooms

To help manage your usage of AI Labeling Tokens, you can exclude specific conversations from being processed:

Exclude Chats in Rooms: Select one or more chat rooms where the metric should not be applied. Chats originating from these rooms will be skipped. This is recommended for rooms that generate low-value or high-volume, repetitive chats and testing rooms.

 

Viewing Results in the Reporting Dashboard

Once your AI Chat Labeling metric is created and actively processing data (or receiving data via API), you can add it to your own Reporting Dashboard for visualization and analysis.

1. Navigate to your Reporting Dashboard at https://reporting.giosg.com/my-dashboard
2. Add Cards: Locate the three-dot menu (...) on the top right corner of the dashboard page.
3. Select "Add cards" from the menu.
4. Choose Metrics: A list of available metric cards will appear. Select the AI Chat Labeling metric(s) you just created and click the plus icon.
5. Visualization: You can choose the visualization type for your card that you think is the most suitable. Note that not all visualization types are available for all metric types.
6. The card will be added to your dashboard, allowing you to view the results, trends, and data distribution based on the labels and values collected.
7. Note that the dashboards are user specific so you can only add the metrics you are interested in.

 

Open the list of metrics:

 

Select the metric(s) to add:

Select the visualization type for the results:

 

Data Source: API Integration

When the Data Source is set to API, the metric's value is provided by an external system rather than being calculated by the AI. This is ideal for integrating existing business data (like an NPS score or lead quality score) into your reporting alongside your chat data.

1. Configuration and API Key

When you set the Data Source to API for a metric, the system generates a unique endpoint for you to post data to.

The bottom of the metric editing page will display the required API endpoint and an example of the expected JSON payload.

Note: The unique Metric ID and the API Endpoint URL will be displayed in this section. The endpoint is secured and requires an API Key authentication.

2. Submitting Metric Data

To update a metric's value for a specific chat, you must make a POST request to the provided API Endpoint. Provided JSON payload can be copied from the bottom of the page. Note that the exact payload structure depends on the type of the metric.