Imagine effortlessly bridging the gap between outdated Fortran and modern C++. Researchers are tackling the complex challenge of automated code translation, and a new technique is showing promising results. Large Language Models (LLMs), known for their text generation prowess, have shown potential in translating code. However, they often stumble with the nuances of different programming languages and complex code structures. This new research introduces a clever twist: Retrieval-Augmented Generation (RAG). Think of it as giving the LLM a library of code translation examples to learn from in real-time. Instead of blindly translating, the LLM can now consult relevant examples, gaining a deeper understanding of the translation process. This method significantly improves the quality of the translated code, especially when dealing with the intricacies of Fortran to C++ conversion. Experiments with various LLMs, including open-source models like Starcoder and commercial giants like GPT-4, show that RAG consistently enhances performance. This research has exciting real-world implications. Modernizing legacy systems written in older languages like Fortran is a major undertaking, often requiring significant manual effort. This new approach can automate much of the process, saving time and resources. While the initial results are promising, challenges remain. Building robust datasets of paired Fortran and C++ code is crucial for training and evaluating these models. The research suggests that a combined strategy of improved datasets and refined RAG techniques could revolutionize how we maintain and modernize our software, ensuring that valuable legacy code remains relevant in the fast-evolving world of technology.