With the great progress in deep learning and natural language processing, many completion tools are proposed to help practitioners efficiently fill in various fields in software document. However, most of these tools offer their users only one option and this option generally requires much revision to meet a satisfactory quality, which hurts much practicality of the completion tools. By finding that the beam search model of such tools often generates a much better output at relatively high confidence and considering the interactive use of such tools, we advise such tools to offer multiple high-confidence model outputs for more chances of offering a good option. And we further suggest these tools offer dissimilar outputs to expand the chance of including a better output in a few options. To evaluate our whole idea, we design a clustering-based initial method to help these tools properly offer some dissimilar model outputs as options. We adopt this method to improve nine completion tools for three software document fields. Results show it can help all the nine tools offer an option that needs less revision from users and thus effectively improve the practicality of tools.