Training Pega Email Bot with Pega GenAI
1 タスク
10 分
シナリオ
Your customer, U+ Bank wants to use Pega Email Bot™ to respond to customer problems and speed up business processes seamlessly. As an Email Developer, you are tasked to help train the already built email bot by adding training records to interpret emails and detect the correct information, such as topics and entities.
Use the following table provides the credentials you need to complete the challenge:
| Role | User name | Password |
|---|---|---|
| Email Developer | Emailadmin | password123! |
Your assignment consists of the following task:
Task: Train the email bot to understand topics
In the Training data tab of the email bot, click Add records using GenAI to help train the email bot by adding training records to interpret emails.
チャレンジ ウォークスルー
詳細なタスク
1 Train the email bot to understand topics
- Log in to Infinity Studio as an Email Developer:
- In the User name field, enter Emailadmin.
- In the Password field, enter password123!.
- In the navigation pane of Infinity Studio, click Channels, and then click the Channels Landing Page icon.
- On the Channels landing page, click My Email Bot to update the Channel configuration.
- Click the Training data tab.
- To add training records, click Add records using GenAI.
- From Topic list, select the Address Change case and then set the number of records to 5.
- Enter the following sample email content:
Hi,
I have a new address. It is 55 Elm Drive, Allston, MA 02134. Can you update my account to reflects this change?
Thanks,
Frederic
The following figure shows the completed dialog:
- Click Create record.
Pega GenAI uses the sample data to generate five similar training records and displays the records for your review. You can see the classification details in the right pane.
In the NLP analysis section, the Language model, Topic, and any entities present are displayed on the bottom tile. The training data text is displayed in the upper tile.
- Under the Language list, click Select all to select all the records, and then click Mark reviewed to add the records to the queue.
- To the right of Add records, click the More icon, and then select Build Model to rebuild the natural language processing (NLP) model.
If the model build is successful, a message that shows the new F-Score is displayed at the top.
- Click Save.