Imagine a customer support chatbot struggling to translate a user's frustrated Korean into helpful English. Getting the language right is just half the battle; capturing the *context* of the conversation is key. New research shows how adding conversation summaries and recent dialogue history dramatically improves LLM translation quality, particularly in tricky customer support scenarios. Researchers tested this approach on English-Korean translations using the powerful Gemma-2-27B-it model. By feeding the LLM a short summary of the earlier conversation and the raw text of the two most recent exchanges, they gave it a much better grasp of the ongoing situation. This allowed the model to produce translations that were not just grammatically correct, but also maintained the tone and intent of the original message, even accounting for common typos in chat conversations. The results were impressive, showing significant boosts in translation accuracy as measured by both human evaluators and automated metrics. This approach offers a practical solution to the long-standing challenge of translating nuanced, informal language, paving the way for more helpful and human-like customer support bots and other conversational AI applications. However, challenges remain. The researchers noted occasional instances of the model producing translations in unexpected languages like French or Turkish, a quirk attributed to its multilingual training. Future research will explore how to refine these models for even greater context sensitivity and eliminate these multilingual hiccups. As LLMs become increasingly integrated into our digital lives, ensuring they understand not only *what* we say but also *why* we say it is crucial for creating truly helpful and effective AI.