Imagine a world where your smart devices could analyze complex information without relying on the cloud. This is the promise of "edge computing," where data processing happens directly on your device, offering speed and privacy. Researchers are now exploring how to combine this power with the intelligence of Large Language Models (LLMs). LLMs like ChatGPT excel at understanding language but deploying them on resource-limited devices is challenging. This new research proposes a method called RED-CT, which allows us to train smaller, specialized AI models (edge classifiers) using data initially labeled by powerful LLMs. Think of it like a master chef (the LLM) providing initial guidance to a talented apprentice (the edge classifier). The apprentice then refines their skills with the help of a more experienced chef. Instead of having humans tediously label all the data, RED-CT uses a clever trick: it pinpoints the LLM's "uncertain" labels and has humans verify only those, dramatically saving time and effort. This study applied RED-CT to tasks like detecting stance, misinformation, ideology, and even humor in online text. Surprisingly, the smaller edge classifiers often *outperformed* the LLMs that trained them. The study shows that we can bring the power of LLMs to the edge, even with limited resources. This approach opens doors to new applications in computational social science, allowing researchers and analysts to quickly analyze large datasets directly on their devices, even offline. This is just the beginning. As LLMs and edge computing evolve, methods like RED-CT will become even more crucial, bringing intelligent, private, and efficient computing to our fingertips.