The migration and reengineering of existing variants into a software product line (SPL) is an error-prone and time-consuming activity. Many extractive approaches have been proposed, spanning different activities from feature identification and naming to the synthesis of reusable artefacts. In this paper, we explore how large language model (LLM)-based assistants can support domain analysts and developers. We revisit four illustrative cases of the literature where the challenge is to migrate variants written in different formalism (UML class diagrams, Java, GraphML, statecharts). We systematically report on our experience with ChatGPT-4, describing our strategy to prompt LLMs and documenting positive aspects but also failures. We compare the use of LLMs with state-of-the-art approach, BUT4Reuse. While LLMs offer potential in assisting domain analysts and developers in transitioning software variants into SPLs, their intrinsic stochastic nature and restricted ability to manage large variants or complex structures necessitate a semiautomatic approach, complete with careful review, to counteract inaccuracies.