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Correcting training data in an email bot


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When you want to improve the ability of Pega Email Bot to detect topics, language, and entities, you can review and correct the training data in the system. By correcting the training data and rebuilding the text analytics model, you improve the artificial intelligence of the email bot and teach it to more accurately detect the desired information in emails. The system can then suggest the right business case or email response, based on the detected information.

For example, an email bot detects a request for a car insurance quote in an email by identifying the relevant topic and entities. The email bot can then create a business case for a car insurance quote and send an automatic reply.
Enable recording of training data. For more information, see Enabling the training data recording for an email bot.

When you enable the recording of training data and the email bot receives an email, the system saves the email as a training record. (You can also manually create a training record that contains sample information for an email from a user). Once you correct the data detected in the training record, you mark the record as reviewed. The system then uses the reviewed training data to rebuild the improved text analytics model.

Teach the email bot the reviewed and corrected training records by rebuilding the text analytics model. For more information, see Applying changes to a text analytics model for an email bot.

  • Creating training data manually for an email bot

    To ensure that Pega Email Bot interprets emails in the correct way, manually create training records in the system. Training records provide valuable information for the email bot that strengthens the artificial intelligence algorithms and improves the accuracy of your text analytics model. By training the model, you ensure that the email bot detects the correct topics, entities, and language.

  • Training data for the Email channel

    To use Pega Email Bot in your application to seamlessly respond to user problems, train the system to recognize different user input in emails, such as help requests or issues. When you train the data for the email bot, the system learns from training records, improves the artificial intelligence algorithms, and provides better responses to user input.

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