Postgres, known for its versatility and extensibility, is widely used for projects ranging from small side ventures to large-scale systems. However, its flexibility can lead to issues as schemas grow and more engineers interact with them, often resulting in subtle mistakes that affect performance and maintainability. Traditionally, senior engineers conducted thorough code reviews to catch such errors, but this is not scalable. Large language models (LLMs) have emerged as a promising tool for automating these reviews by enforcing database rules through prompts and adapting to specific schemas and ORM styles. They can identify issues missed by traditional linters, such as runtime problems or schema mismatches, provided that explicit rules are established to guide their evaluations. These rules focus on preventing table locks with concurrent indexes, avoiding downtime when dropping columns, ensuring foreign keys are indexed, maintaining table column consistency, avoiding redundant indexes, and ensuring queries are indexed. By incorporating these best practices into LLM-driven reviews, teams can scale their code review processes efficiently, reducing the likelihood of errors and enhancing overall system reliability.