Skip to main content
Back to XO GPT Model Specifications Use this guide to provide effective feedback on XO GPT models and understand how Kore.ai incorporates it.

​
How to Provide Effective Feedback

​
1. Assess Frequency

  • Measure how often issues occur across a wide range of samples.
  • Prioritize frequent issues—they have the most impact on model performance.
  • Use occurrence rates to decide whether an issue needs immediate attention or further monitoring.

​
2. Identify Recurring Patterns

  • Focus on consistent issues, not isolated errors (unless the issue is critical).
  • Document patterns for a more accurate evaluation of model behavior.

​
3. Categorize the Issue

​
4. Identify the Use Case

Specify the context—for example: Address Update, Medical Claim Processing, Customer Support Queries, or Financial Transactions.

​
5. Lock Sample Sets

  • Once a problematic sample set is identified, use it consistently for testing.
  • Track new issues separately with fresh sample sets to avoid overlap.

​
6. Submit a Support Ticket

When recurring issues are identified, submit a support ticket with the sample set, identified patterns, and the following information:
When sharing data, mask all sensitive information. Don’t include real customer data, it’s used for training purposes. Kore.ai strictly prohibits the use of actual customer data for model training.
All processes and outputs adhere to company-wide standards. Changes beyond these guidelines may not be possible. Contact the support team for questions about data handling.

​
Feedback Workflow

​
Continuous Improvement

Each feedback round should focus on new issues and use updated sample sets for accurate evaluation. Ongoing feedback ensures the model stays aligned with your evolving business needs.