Matching data schemas is a bit like translating languages for databases. It's crucial for data integration, but it's a complex process that data engineers often tackle manually. Imagine trying to merge databases that store patient information, but one calls "admittime" what the other calls "visit_start_date." That's where schema matching comes in. Traditionally, this involves comparing attribute names, looking at data values, and using other clues. But what if you could use AI, specifically Large Language Models (LLMs), to automate this? Researchers explored exactly this in a new study, using real-world health data schemas from MIMIC-IV and OHDSI OMOP. They tested different ways of prompting LLMs (called "task scopes") to find matches between schema elements. Some prompts included only individual attribute names, while others offered the LLM more context, like entire schemas. They discovered that LLMs could indeed find matches, even outperforming traditional string-matching methods. Interestingly, giving the LLM too little or *too much* information actually lowered performance. The "sweet spot" seemed to be providing a moderate amount of context. This suggests LLMs need just enough information to understand the relationships between attributes without getting bogged down in complexity. The study's most promising finding? Combining different prompting strategies significantly improved the LLM's ability to find valid matches. This means that LLMs, while not perfect, can boost the efficiency of schema matching, freeing up data engineers for more complex tasks. Future research could focus on how LLMs can explain their reasoning, enhancing trust and allowing users to refine their schema matching further. This could potentially transform how we integrate data across different systems, making the process faster, more accurate, and ultimately, unlocking the power of combined datasets for better insights.