To build a ChatGPT detector, you would first need to collect a large amount of text that was generated by ChatGPT. This could be done by providing a variety of different prompts to the ChatGPT model and collecting the responses.
Next, you would need to train a machine learning model on this text. The most common approach for this kind of task is to use a deep learning model, such as a recurrent neural network (RNN). The RNN would be trained to take in a piece of text and predict whether it was written by ChatGPT or not.
As for the accuracy of such a model, it would depend on a variety of factors, including the quality and quantity of the training data, the specific model architecture and hyperparameters used, and the skill of the person training the model. With a large and diverse training dataset, and appropriate model design and training, it is likely that a ChatGPT detector could achieve relatively high accuracy.