Large language models (LLMs) have taken the world by storm, but they're not without their limitations. They can sometimes hallucinate (generate incorrect or nonsensical information) and struggle with the demands of complex reasoning. Making LLMs more trustworthy and efficient is crucial for their wider adoption, especially when dealing with the structured world of databases. This challenge is being tackled from multiple angles. Imagine LLMs as evolving beings, starting with internal improvements to their core architecture and training methods, like refining a child's education. Techniques like instruction tuning and reinforcement learning from human feedback help align LLMs with user intentions and improve the accuracy of generated outputs. Then, like giving them eyes and hands, researchers are connecting LLMs to external resources like knowledge graphs, vector databases, and APIs. This "retrieval augmentation" allows LLMs to access up-to-date information and interact with the broader world beyond their initial training data. The next step is giving LLMs a "brain"—the ability to reason and make decisions autonomously. This involves techniques like self-reflection, where LLMs can evaluate and refine their own outputs, and multi-path reasoning, where they explore multiple lines of thought before arriving at an answer. The intersection of LLMs and databases presents exciting new possibilities. LLMs can assist database administrators (DBAs), optimize queries, and even translate natural language into SQL. Conversely, database technologies are being adapted to improve LLM inference, the process of generating text. Managing the key-value (KV) cache, which stores intermediate computations within the LLM, is akin to managing data in a database. Techniques like paging and virtual memory, borrowed from database management, improve efficiency and reduce memory fragmentation. The future of this partnership involves deeper integrations, such as building cost models for LLM operations and optimizing queries across both relational data and LLM calls. Imagine a future where databases can seamlessly integrate LLM capabilities, leading to more powerful and efficient data analysis and interaction. This emerging field of “neuro-symbolic” systems promises to combine the strengths of both neural networks (like LLMs) and symbolic reasoning (like traditional database operations) to unlock entirely new ways of interacting with data.