Initial Testing and Go Live

Congratulations on setting up your xBot instance and creating your first AI Flow! Before you go live, it’s essential to thoroughly test your setup to ensure that everything is working as expected. This page will guide you through the steps of initial testing, troubleshooting, and finally making your xBot available to users.

Step 1: Initial Testing

1.1 Test the AI Flow

• Run Simulated Queries:

  • Use the testing tools within the AI Flow Designer to simulate various user queries.

  • Ensure that each query triggers the correct AI Flow and that the responses are accurate and relevant.

  • Example: If you created a flow for checking order status, simulate a user asking, "What’s the status of my order?"

• Test All Decision Points:

  • Make sure to test all possible branches of your AI Flow, especially the conditional logic that handles different scenarios.

  • Example: If your flow branches based on keywords like "order" or "support," test queries that should trigger each branch.

1.2 Validate Communication Channels

• Test Each Channel:

  • Send test messages through all configured communication channels (e.g., Zalo, Facebook Messenger, MS Teams).

  • Verify that messages are correctly received, processed by the appropriate AI Flow, and that the responses are sent back to the correct channel.

• Cross-Platform Compatibility:

  • Ensure that xBot can handle messages and respond consistently across different platforms.

  • Example: A query sent through Zalo should trigger the same response as one sent through Facebook Messenger.

For more details on testing across channels, see the Message Type Compatibility Guide.

1.3 System Diagnostics

• Check System Logs:

  • Review system logs for any errors or warnings that occurred during testing.

  • Address any issues found in the logs to ensure a smooth operation when xBot goes live.

• Monitor Performance Metrics:

  • Track key performance metrics like response time, accuracy, and system load to ensure xBot is performing optimally.

  • Example: Measure how quickly xBot processes and responds to queries under different load conditions.

1.4 User Acceptance Testing (UAT)

• Involve Key Stakeholders:

  • Conduct a round of user acceptance testing by involving stakeholders who will be using xBot.

  • Gather feedback on the user experience and make any necessary adjustments before going live.

• Final Adjustments:

  • Based on feedback from UAT, fine-tune your AI Flows, configurations, and settings to ensure everything is working as desired.

Step 2: Preparing to Go Live

2.1 Review and Finalize Configurations

• Revisit Security Settings:

  • Double-check all security settings, including user roles, permissions, and data encryption configurations.

  • Ensure that all sensitive data is protected and that access controls are in place.

• Ensure Documentation is Up-to-Date:

  • Make sure that all relevant documentation, including user guides and troubleshooting steps, is updated and accessible to your team.

2.2 Backup Your Configuration

• Create a Backup:

  • Before going live, create a backup of your xBot configuration, including AI Flows, user data, and system settings.

  • This ensures that you can restore your system quickly in case of any issues post-launch.

Step 3: Go Live

3.1 Activate xBot

• Enable xBot for Users:

  • In the Admin Dashboard, switch your xBot instance from test mode to live mode.

  • Ensure that all communication channels are activated and ready to receive live queries.

• Monitor Initial Interactions:

  • Closely monitor the first interactions after going live to ensure that xBot is handling queries correctly.

  • Use real-time analytics to track performance and quickly address any unexpected issues.

3.2 Post-Launch Support

• Provide Support Channels:

  • Set up a dedicated support channel for users to report any issues or provide feedback after xBot goes live.

  • Example: Create a support email or a specific chat channel where users can reach out.

• Continuous Monitoring:

  • Continue to monitor xBot’s performance regularly, especially during the initial days after launch.

  • Schedule regular check-ins to review system logs, user feedback, and performance metrics.

3.3 Iterate and Improve

• Collect User Feedback:

  • Gather feedback from users on their experience with xBot, focusing on areas like response accuracy, ease of use, and overall satisfaction.

  • Use this feedback to make iterative improvements to your AI Flows and system configurations.

• Plan for Updates:

  • Keep track of any planned updates or enhancements to xBot, and schedule them during low-traffic periods to minimize disruption.

Conclusion

By following these steps, you’ll ensure that your xBot instance is thoroughly tested and ready to go live with confidence. Remember, going live is just the beginning – continuous monitoring and improvement will help you maintain optimal performance and user satisfaction.

If you encounter any issues during the go-live process, please refer to the Troubleshooting and Support section or contact your support team for assistance.