Operating at the intersection of generative AI (artificial intelligence), machine transla-tion (MT), and literary translation, this paper examines to what extent prompt-driven post-editing (PE) can enhance the quality of ma-chine-translated literary texts. We assess how different types of instruction influence PE performance, particularly focusing on lit-erary nuances and author-specific styles. Situated within posthumanist translation theory, which often challenges traditional notions of human intervention in translation processes, the study explores the practical implementation of generative AI in multilin-gual workflows. While the findings suggest that prompted PE can improve translation output to some extent, its effectiveness var-ies, especially in literary contexts. This highlights the need for a critical review of prompt engineering approaches and empha-sizes the importance of further research to navigate the complexities of integrating AI into creative translation workflows effective-ly.