The evolution of machine translation (MT) has seen significant advancements in data cleaning and post-editing methodologies, but numerous cases requiring semantic comprehension have still necessitated human intervention—until the emergence of Large Language Models (LLMs). In our research, we have explored an innovative application of Generative AI (Gen AI) to adapt bilingual content’s target segments from a formal to an informal register, in scenarios where the source language lacks explicit grammatical markers for formality and thus is grammatically bivalent in that sense. In this session, we will demonstrate how LLMs, enhanced by supplementary methodologies such as fine-tuning and combined with other, more legacy language models, can efficiently perform this formality adaptation task. We aim to showcase best practices for leveraging Gen AI in adapting bilingual content registers, highlighting the potential for cost reduction and quality enhancement in translation processes.