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How we handle low ratings in #Mapflow
When a user rates a result with 1 or 2 stars ⭐️, our support team reviews the case to assess whether a credit refund is warranted. In many instances, low ratings stem from mismatched expectations about the model’s capabilities. In others, the output may be bad—this can happen due to the complexity of globally stable models that must handle a wide variety of patterns and contexts.
Poor-quality input data can also impact the final results, making them less usable. If, upon review, we confirm that the result was of insufficient quality, we refund the credits used for that specific process. Users will receive a notification once the refund is done.
In some cases, we also follow up with an explanation—especially when we’ve identified a clear reason for the poor result or when the results can be improved by adjusting the model parameters.

We welcome all feedback, whether positive or negative—it helps us improve and better align with your expectations. 🤗
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How we handle low ratings in #Mapflow
When a user rates a result with 1 or 2 stars ⭐️, our support team reviews the case to assess whether a credit refund is warranted. In many instances, low ratings stem from mismatched expectations about the model’s capabilities. In others, the output may be bad—this can happen due to the complexity of globally stable models that must handle a wide variety of patterns and contexts.
Poor-quality input data can also impact the final results, making them less usable. If, upon review, we confirm that the result was of insufficient quality, we refund the credits used for that specific process. Users will receive a notification once the refund is done.
In some cases, we also follow up with an explanation—especially when we’ve identified a clear reason for the poor result or when the results can be improved by adjusting the model parameters.

We welcome all feedback, whether positive or negative—it helps us improve and better align with your expectations. 🤗

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