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Generative large language models (LLMs) have demonstrated exceptionalproficiency in various natural language processing (NLP) tasks, includingmachine translation, question answering, text summarization, and naturallanguage understanding.  To further enhance the performance of LLMs in machine translation, weconducted an investigation into two popular prompting methods and theircombination, focusing on cross-language combinations of Persian, English, andRussian. We employed n-shot feeding and tailored prompting frameworks. Ourfindings indicate that multilingual LLMs like PaLM exhibit human-like machinetranslation outputs, enabling superior fine-tuning of desired translationnuances in accordance with style guidelines and linguistic considerations.These models also excel in processing and applying prompts. However, the choiceof language model, machine translation task, and the specific source and targetlanguages necessitate certain considerations when adopting prompting frameworksand utilizing n-shot in-context learning.  Furthermore, we identified errors and limitations inherent in popular LLMs asmachine translation tools and categorized them based on various linguisticmetrics. This typology of errors provides valuable insights for utilizing LLMseffectively and offers methods for designing prompts for in-context learning.Our report aims to contribute to the advancement of machine translation withLLMs by improving both the accuracy and reliability of evaluation metrics.

Machine Translation with Large Language Models: Prompt Engineering for Persian, English, and Russian Directions