Sorry about the confusion! Prodigy will never modify an existing dataset, so your data will never change if you review it. After rewiewing, you typically end up with two datasets: the original data with potential duplicates and conflicts that you load in, and the reviewed dataset with one final version of each annotation that you save your reviews to. The goal of the
review workflow is to show you all versions of a given example grouped together, and let you have the final decision. The resulting data saved to the new dataset will be one final annotation for each input example. You typically want to save this to a new dataset, and then use it to train your model.
You should always be able to start and stop the review workflow, or keep adding more reviewed final decisions to your data as new annotations come in. Just keep in mind that by default, Prodigy will skip examples that are already in the dataset you're saving to: so if you've already reviewed the same example before, you won't be asked about it again.
(Btw, this thread is mostly about looking at your previous reviewed examples, which seems to be slightly different from the use case you describe? It sounds like you probably just want to keep running
review with the same input and output dataset until you're done and have reviewed all annotations.)