This chapter explores the innovative application of large language models (LLMs) in translator training, focusing on the use of few-shot prompts and chain-of-thought prompting. It proposes a novel approach that integrates metalanguages and concepts of Translation Studies into prompt engineering, moving beyond traditional natural language processing goals of improving machine translation quality. The chapter demonstrates how this method can create interactive and engaging learning experiences for translation students, allowing them to explore various translation strategies and develop critical thinking skills. Through concrete examples, the chapter illustrates the potential of LLMs to generate diverse translation variations and provide insightful analyses of translation processes. While acknowledging limitations and the need for critical evaluation, the research emphasises the positive and proactive possibilities of LLMs in translator training. This approach not only bridges the gap between translation theory and practice but also opens new avenues for autonomous learning and the development of essential skills for future translators in the AI era.