This research explores how Large Language Models can transform database query optimization by generating more efficient execution plans than traditional optimizers.

• LLMs can effectively plan and make decisions within the complex, exponentially growing query plan space
• The approach reduces reliance on rigid cost models built with heuristics and empirical tuning
• Results show potential for generating superior query execution plans compared to conventional methods
• Represents a significant paradigm shift in database management system optimization

This engineering breakthrough could dramatically improve database performance in enterprise systems, potentially reducing processing time and computational costs for data-intensive applications.

Can Large Language Models Be Query Optimizer for Relational Databases?