# Improving a model with Model feedback
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The Model Feedback solution enables you to give feedback on your model's predictions and make improvements by using human verification. Depending on the use case, you can be successful with a training dataset that has only a few images. A larger annotated training set might be required to build a more accurate model. Using the Model Feedback solution, you can create a larger dataset through model assistance.

To install and configure the Model Feedback solution, see [Model Feedback Solution](https://github.com/aws-samples/amazon-rekognition-custom-labels-feedback-solution).

The workflow for continuous model improvement is as follows:

1. Train the first version of your model (possibly with a small training dataset).

1. Provide an unannotated dataset for the Model Feedback solution.

1. The Model Feedback solution uses the current model. It starts human verification jobs to annotate a new dataset.

1. Based on human feedback, the Model Feedback solution generates a manifest file that you use to create a new model.