Imagine training a powerful AI model, only to find it's too resource-intensive for everyday use. That’s the problem many developers face. Large Language Models (LLMs) like GPT-3 offer incredible capabilities, but their immense size and computational demands make them impractical for smaller companies or individual projects. This new research tackles this challenge by exploring how the “knowledge” within these massive LLMs can be transferred to smaller, more efficient models called pre-trained Language Models (LMs). Think of it like distilling the wisdom of a seasoned expert into a quick guide. Researchers used LLMs to generate specialized datasets for tasks like identifying bugs and finding duplicated code. Then, they used these LLM-created datasets to train the smaller LMs. The results were impressive. These lightweight LMs, after learning from data generated by the LLMs, saw performance improvements up to 58% for bug detection and 6% for duplicate code identification. This breakthrough suggests a new paradigm for AI development: using resource-heavy LLMs to boost the capabilities of smaller, more accessible LMs. This could democratize access to powerful AI tools, allowing developers with limited resources to leverage the vast knowledge embedded within LLMs. While this research is still in its early stages, it opens up exciting possibilities for future applications. Challenges remain, particularly regarding efficient selection of the most useful data from LLMs. Nevertheless, this “knowledge transfer” technique could pave the way for a more inclusive AI landscape, where cutting-edge capabilities are within reach of everyone.