> ## Documentation Index
> Fetch the complete documentation index at: https://koreai.mintlify.app/llms.txt
> Use this file to discover all available pages before exploring further.

# XO GPT Feedback Submission

<Badge icon="arrow-left" color="gray">[Back to XO GPT Model Specifications](/ai-for-service/generative-ai-tools/xo-gpt-module#xo-gpt-model-specifications)</Badge>

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

| Category              | Description                                                        |
| --------------------- | ------------------------------------------------------------------ |
| **Misinterpretation** | Model misunderstands the intent or meaning of the input.           |
| **Negation**          | Model incorrectly processes negation terms (e.g., "not," "never"). |
| **Omission**          | Model fails to include critical information in the response.       |
| **Redundancy**        | Model provides excessive or unnecessary information.               |
| **Repetition**        | Model repeats phrases or ideas unnecessarily.                      |

### 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](https://support.kore.ai/hc/en-us/requests) with the sample set, identified patterns, and the following information:

| LLM Input                              | Actual Response                | Expected Response                              | Error Category    | Use Case       | Comments                                    |
| -------------------------------------- | ------------------------------ | ---------------------------------------------- | ----------------- | -------------- | ------------------------------------------- |
| "Update my address to 1234 Elm St"     | "Your address has not changed" | "Your address has been updated to 1234 Elm St" | Negation          | Address Update | Model failed to process negation correctly. |
| "Process my medical claim for surgery" | "Your claim has been denied"   | "Your claim is under review"                   | Misinterpretation | Medical Claim  | Incorrect understanding of claim status.    |

<Note>
  When sharing data, mask all sensitive information. Do not include real customer data—it is used for training purposes. Kore.ai strictly prohibits the use of actual customer data for model training.
</Note>

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

| Step                   | Action                                                                 |
| ---------------------- | ---------------------------------------------------------------------- |
| **1. Submit Feedback** | Provide detailed feedback with examples, context, and expected output. |
| **2. Analysis**        | Kore.ai reviews the feedback and defines the scope of improvements.    |
| **3. Data Collection** | Additional data may be requested to improve model accuracy.            |
| **4. Model Update**    | The model is retrained and refined based on feedback and new data.     |

***

## 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.