It is not sufficient to add only a few instances of the same training data. For each topic, create at least 20 records in the training sample so that the system learns how to correctly respond to the entered text. Distribute records evenly across topics, otherwise the model will be biased toward the overrepresented topic. For more information, see Best practices for creating categorization models.

1. In the navigation pane of App Studio, click Channels.
2. In the Current channel interfaces section, click the icon that represents your existing Legacy Webchat channel that includes the Insurance Quote case type.
3. In the channel, click the Training data tab, and then click Add records.
4. In the Create new training record window, in the Topic field, press the Down arrow key and select: insurance
5. In the next field, enter the following sample user input inquiring about car insurance: I need a car quote.

   

6. Click Create record.
7. Add more instances of the same training record by repeating steps 5 through 6 five more times.
8. In the next field, enter the following sample user input inquiring about insurance for a specific car make and model insurance: I need a car insurance quote for my Ford Mustang GT 2017.

   

9. Click Create record.
10. Add more instances of the same data record by repeating steps 8 through 9 five more times.
11. Click Close.

In this example, you will associate the #Carmakeent and #Carmodelent entities with the car make and model in the user input. These entities are already mapped to the .carMake and .carModel properties that are defined for the Insurance Quote case.

1. On the Training data tab, select the training data record with the text: I need a car insurance quote for my Ford Mustang GT 2017
2. In the Training data correction window, select and right-click Ford, and then click #Carmakeent.
3. Select and right-click Mustang GT, and then click #Carmodelent.

   

4. Map more instances of the training data with the same text by repeating steps 1 through 3 five more times.

1. On the Training data tab, select the checkboxes for the six instances of the text: I need a car quote

   

2. Click Mark reviewed.
3. Select the checkboxes for the six instances of the text: I need a car insurance quote for my Ford Mustang GT 2017
4. Click Mark reviewed.

1. On the Training data tab, click the More icon and then Build model.

   You build the model by using only training data that was marked as reviewed.

   

   Result: The system successfully updates the model with the six training data records of each type that you created in the previous section.
2. Click Save.

1. On the Behavior tab, in the preview console, enter the following text: I need a car quote.
2. Display more information about the conversation and the case by turning on the Show analysis switch.

   

   Result: The chatbot correctly responds, associating the text that you entered for the insurance topic with a confidence of 100 percent. The system also starts case I-122 and asks the first question about the make of the car.
3. Click Reset.
4. Enter the following text: I need a car insurance quote for my Ford Mustang GT 2017

   

   Result: The IVA correctly responds by associating the text that you entered with the insurance topic with a confidence of 100 percent. The system also starts case I-123. In addition, the car make and model that you entered, Ford and Mustang GT, are extracted and automatically mapped to the car make and model properties for the case. As a result, the IVA also skips the questions about the car make and model.