I would generally advise not to use imputed scores to process your dataset. I always calculate PCs, perform clustering and reduce dimensions using the variable genes you discover using FindVariableGenes(), without any imputation. That being said, the imputation that Seurat offers is a practical solution to generate output plots. use.imputed is not always specifically noted in the help files of functions, but you can try adding it to functions that generate output, and it will work in a lot of cases.
What I normally do is this:
dataset <- Seurat::AddImputedScore(object = dataset, genes.use = email@example.com, genes.fit = c("GOI1", "GOI2", etc), do.print = TRUE)
The do.print variable plots a message for each gene that was imputed, which I personally like. You can set it to FALSE if you want.
This imputes the specific genes that you are interested in modelling them on the variable genes that you identified in your dataset. This normally works quite well for me. The imputed scores are stored in the @imputed tab of your seurat object and can be queried as such. You can add more genes as you work. I would advise against imputing all genes, as the process is not very fast.
Functions that work with imputation (add "use.imputed = TRUE") as far as I know them: FeaturePlot(), VlnPlot(), GenePlot(), RidgePlot(). Maybe there's more, check the scripts on the Seurat github, or just test as you work
Hope this helps,