Large language models (LLMs) are impressive, but they often struggle with specialized tasks. Think of them as brilliant generalists—great at writing poems or summarizing text, but not so great at, say, diagnosing medical conditions. New research explores a clever way to make these LLMs more specialized without the usual expensive retraining. Imagine giving an LLM a team of tiny expert AIs to consult. That's the basic idea behind “Ensemble SuperICL,” a technique that lets an LLM leverage the wisdom of multiple smaller, specialized models. These smaller models, called SLMs, act like expert consultants, each offering their predictions and confidence levels on a given task. The LLM then considers these diverse opinions, learns which SLMs are reliable, and synthesizes a final, more accurate answer. Researchers tested this approach on several language tasks, including sentiment analysis, paraphrase detection, and even a medical subject classification task. The results? The LLM, aided by its tiny AI team, significantly outperformed both the LLM working alone and the individual SLMs. Surprisingly, even when some of the SLMs were individually weak, their combined input helped the LLM make better decisions. This is particularly exciting for tasks requiring domain expertise, like medical diagnosis or legal analysis, where gathering labeled data is costly and time-consuming. Ensemble SuperICL offers a promising way to make LLMs more specialized and accurate for complex real-world tasks without the need for extensive retraining. While there are still questions about optimizing the size and selection of these AI teams, this research points to an exciting future for more efficient and adaptable LLMs.