Imagine teaching a small, eager-to-learn AI model the complex grammar of a rare language. It's a challenge, especially when data is scarce. Researchers have found a clever way to boost these compact models using the brawn of Large Language Models (LLMs) and a dash of linguistic know-how. The secret lies in a technique called Retrieval-Augmented Generation (RAG). It's like giving the small model access to a smart tutor. When the compact model stumbles over a grammatical puzzle, RAG pulls relevant snippets from grammar guides and feeds them to an LLM. The LLM acts as an expert interpreter, using these snippets to correct and refine the small model's initial guesses. This process not only improves accuracy but also provides explanations for each correction, like a teacher showing their work. This is a big win for linguists working with endangered languages, where data is often limited. The results are impressive, achieving state-of-the-art performance in morpheme glossing for Uspanteko (a Mayan language) and Arapaho. This research opens up exciting possibilities for making AI more efficient and transparent, particularly in low-resource settings. It’s like unlocking the potential of small AI models, giving them a boost with the power of LLMs and linguistic expertise. While there are challenges, such as the reliance on grammar quality and scaling to larger datasets, this approach holds promise for democratizing access to AI for languages with limited data.