Database tuning, the intricate art of optimizing performance by tweaking system knobs, has long been a realm of specialized expertise and laborious trial-and-error. But what if there was a way to automate this complex process? Recent research explores the potential of Large Language Models (LLMs) to act as virtual database administrators, potentially revolutionizing how we manage data systems. Imagine an LLM sifting through the hundreds of knobs in a database like MySQL, identifying the most impactful ones for a given workload—much like a seasoned DBA would. This is precisely the capability researchers investigated, pitting LLMs against traditional tuning methods and even human experts. The results are intriguing. In experiments using the SYSBENCH benchmark, some LLMs rivaled or even outperformed human DBAs in selecting the most critical knobs for optimization. They demonstrated a keen ability to identify which knobs—like buffer pool size or I/O thread count—truly mattered for a specific task, mirroring the intuition of experienced administrators. Moreover, LLMs showed remarkable efficiency. While traditional methods often require hundreds of iterations to find optimal settings, LLMs could often pinpoint promising configurations within a fraction of the time. This efficiency stems from the LLMs' ability to rapidly process and understand complex database feedback, allowing them to make smarter adjustments with each iteration. Beyond simply suggesting better settings, LLMs also offer greater transparency. Unlike traditional “black box” tuning methods, LLMs can explain their reasoning, providing a clear chain of thought behind each recommendation. This interpretability is crucial for DBAs, empowering them to understand and validate the LLM's suggestions, fostering a collaborative tuning process. This research extends beyond MySQL and SYSBENCH, evaluating LLMs with different databases (like PostgreSQL and TiDB) and varying hardware setups. The results consistently highlight the adaptability of LLMs, requiring only simple prompt adjustments to switch between different environments—a significant advantage over traditional methods that often demand extensive retraining or code modifications. The prospect of LLMs as automated database tuners is compelling. Future research aims to develop end-to-end LLM-driven tuning systems, potentially eliminating the tedious iterations of current approaches and democratizing access to optimal database performance.