Machine translation has made incredible strides, allowing us to communicate across languages with remarkable ease. But this technological marvel largely benefits only a handful of widely spoken languages. For the thousands of less-resourced languages, the massive datasets needed to train sophisticated AI models simply don't exist. Researchers are exploring innovative solutions to this digital language divide, and one intriguing approach involves going 'back to school,' leveraging readily available resources like grammar books and dictionaries. A recent study investigated whether injecting linguistic knowledge directly into large language models (LLMs) could enhance their translation capabilities for low-resource languages. The results offer a promising glimpse into the future of translation. By feeding LLMs grammar books, dictionaries, and a small set of parallel sentences, the researchers saw notable performance gains across 16 diverse languages, including Chokwe, Chuvash, and Wolof. Surprisingly, the grammar books weren't always the key to success. For languages with some existing digital presence, even a small collection of translated sentences proved more beneficial. This suggests that LLMs, pre-trained on vast amounts of text data, may already possess a foundational understanding of these languages. However, for truly under-resourced languages, the grammar books became invaluable teaching tools, significantly boosting the LLM's ability to decipher and translate. This 'back to school' approach faces some hurdles. High-quality grammar books are scarce for many languages, and those available often suffer from digitization errors, making them less effective learning aids. Furthermore, the computational cost of these experiments is substantial, potentially limiting wider exploration and research. Nevertheless, this research provides a compelling proof of concept. It reveals the potential of leveraging existing linguistic resources to bridge the language gap and empower LLMs to translate even the most obscure languages. This could pave the way for a more inclusive future where technology empowers communication and understanding across all linguistic communities. Future research will delve deeper into understanding why grammar books provide the greatest benefit to truly low-resource languages, explore more cost-effective training methods, and develop techniques to clean and refine digitized grammar books for optimal LLM learning.