Imagine trying to understand a vast library of information, not neatly organized into a single text, but scattered across multiple tables, like a complex spreadsheet. That’s the challenge AI faces when dealing with relational databases, the backbone of how we store much of the world’s data. Large Language Models (LLMs), known for their text prowess, traditionally struggle with this structured data format. Enter rLLM, a new tool that bridges the gap between LLMs and relational tables. The core idea is to break down complex AI models, like Graph Neural Networks (GNNs), LLMs, and Table Neural Networks (TNNs), into smaller, interchangeable building blocks. These blocks can then be combined, aligned, and trained together in a mix-and-match fashion, enabling researchers to rapidly create new models specifically designed for relational data. Think of it like LEGOs for AI, where different pieces are combined to build something new. rLLM’s developers provide a simple example method called BRIDGE, which shows how TNNs and GNNs can work together to interpret both the data *within* tables and the relationships *between* them, using 'foreign keys' as connections. Recognizing the lack of readily available data for this type of research, the team created three new relational table datasets (TML1M, TLF2K, and TACM12K). These were built by enriching classic datasets, providing a balanced playground for testing new models and encouraging further exploration of relational table learning. Experiments comparing BRIDGE to other TNNs show its ability to effectively capture the nuances of multi-table information. While still in its early stages, rLLM offers a promising new framework for tackling the complexities of relational data. It opens the door for more sophisticated and efficient analysis of everything from user behavior on e-commerce sites to complex scientific data. It's not about replacing humans, but giving them more powerful tools to unlock insights hidden in plain sight.