This study explores the influence of two prompting strategies on the lexical and syntactic metrics of the LLM-powered machine translations (MTs) of a corpus of 18~British editorials into French as well as their impact on the edit types made by Master’s translation students post-editing from a representative editorial of the corpus, as evaluated using the MTPEAS taxonomy. Quantitatively, the prompt specifically requesting more syntactic and lexical variety leads to significantly higher syntactic and lexical metrics scores in the MTs, but differences remain significant only for lexical metrics in the post-edited versions of the representative editorial. Qualitatively, we show that students post-editing from an MT featuring more idiomatic rephrasings and fewer syntactic calques (as opposed to an MT that is structurally closer to the source text) seem to make fewer edits overall, leave more MT errors unaddressed, and make fewer successful edits.