How does the restriction of robot teachings work?

In this article, we will explore in detail how the restriction of robot teachings work in the Teach Robot tool, an innovative solution that allows hoteliers to teach their virtual assistants new knowledge to enhance the traveler's experience. However, too many teachings can impact the robot's training time, leading to issues in saving the answers and also the robot's performance.

So we will explain the restriction of teachings and how it optimizes the training of robots, ensuring they remain efficient and capable of meeting the needs of hoteliers and travelers.

How Does the Restriction Work?

Restricting teachings is a strategy adopted to ensure that the training process of virtual assistants is efficient and does not result in prolonged waiting times, known as timeouts.

What is a Timeout?

Timeout is a term used to describe the maximum time limit that a system has to perform a specific task. In the context of our Teach Robot tool, when a robot has a large amount of teachings, the time required to process all the information can exceed the established time limit for training, resulting in a timeout that automatically interrupts the ongoing process.

The Importance of Training

Before explaining the restriction of teachings, it's crucial to understand the importance of training virtual assistants. Training is the process through which the robot acquires knowledge and skills to interact efficiently with travelers. The more teachings a robot has, the greater its knowledge of travelers' needs and questions, contributing to a personalized and enhanced experience.

The Relationship Between Teachings and Training

The teachings added by hoteliers consist of a keyword, its synonyms, and variations of sentences related to that keyword. This information is essential for the robot to understand travelers' questions and needs and provide appropriate answers.

However, the relationship between teachings and training time is not linear. The more teachings a robot has, the more complex the task of processing all this information becomes, so that the robot is prepared for future interactions. Training involves the analysis, processing, and continuous adaptation of teachings, making it a computationally intensive operation.

The Restriction of Teachings

To ensure the efficiency of robot training, we have implemented a limitation on the number of teachings a robot can have. This limitation has been set at 500 teachings.

The count of teachings is done by summing up the following components:

• Keyword: Each keyword added to the robot counts as 1;
• Synonyms: Each synonym related to the keyword is included in the sum;
• Variations of sentences: Each variation of a sentence related to the keyword also contributes to the sum to form the teaching.

In this way, we can represent the sum as follows:

Teaching = ((Keyword = 1) + Number of synonyms) x Number of variations of sentences

Good Practices

Now that we understand how restricting the teachings works in the Teach Robot tool, let's highlight some good practices that hoteliers can adopt to optimize tool usage:

• Prioritize relevant teachings: When adding teachings to the robot, prioritize relevant and frequently requested information from travelers. Focusing on common questions will increase the efficiency of the robot and improve the user experience.
• Avoid repetitions: Avoid creating teachings that are very similar to each other, as this can lead to unnecessary counting of teachings and hinder robot training.
• Avoid creating teachings the robot already understands: Before creating new teachings, check all the answers already registered in your robot's questionnaire. They include standard and essential information for the hotel industry. If you want to supplement the answer, you can do so directly in the questionnaire. If the robot doesn't respond to information that is already in the questionnaire, simply contact our support department. To learn more about the robot's activation questionnaire, click here.
• Monitor performance: Regularly monitor the robot's performance to identify possible areas for improvement and adjust teachings as needed.
• Update outdated teachings: As travelers' needs change over time, make sure to update or remove outdated teachings to keep the robot up to date and efficient.

Conclusion

The limitation of teachings in the Teach Robot tool is an essential measure to ensure that the training of virtual assistants is efficient and does not result in prolonged waiting times. By understanding how this restriction works and following the suggested practices, hoteliers can optimize tool usage, offer an enhanced experience for travelers and boost the success of their businesses.

Remember that our support team is available to assist with any questions or needs related to the Teach Robot tool and other incredible solutions we offer to transform the future of the hotel industry.