Optimizing AI Agents through Enjo's Training Feature

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Optimizing AI Agents through Enjo's Training Feature

Enhancing AI Agent Performance

Enjo's Training feature empowers AI Agent administrators to improve the performance of their AI Agents by refining responses to past user interactions. By analyzing past unanswered conversations and taking actionable steps, administrators can enhance the AI Agents' effectiveness in delivering accurate and helpful responses.

Navigating the Training Process

To access the Training feature in Enjo, users need to navigate to the Training tab located within the main menu on the Enjo dashboard. Once in the Training tab, users can select the specific AI Agent they wish to train from a dropdown menu. This allows administrators to focus on training individual AI Agents for targeted improvements.

Understanding Past User Interactions

The Training page presents administrators with a table showcasing past user interactions. Key fields such as the date of the interaction, the AI Agent's interpretation of the query, the AI Agent's response, and available training actions are displayed. This comprehensive view enables administrators to spot patterns and areas for improvement.

Exploring Conversation Details

By clicking on the 'More' button for specific conversations, administrators can access detailed information such as metadata about the message, any attachments related to the query, how the AI interpreted the user's input, the user's original message, and more. This deeper dive into conversations aids in making informed decisions for training enhancements.

Taking Action for Improvement

After reviewing past interactions and conversation details, administrators can take various actions to enhance the AI Agent's responses. Options include adding a new response as a standalone entry in Enjo Answers, linking the query to an existing answer, associating the query with a predefined AI Action for automated workflows, or mapping the query to an existing request form for structured responses. These actions drive continuous improvement in AI Agent performance.

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