SLM-Augmented PostgreSQL Query Optimization

  • Authors

    • Kiran Reddy Sudini Director of Cloud Engineering & Operations, USA. Author

    DOI:

    https://doi.org/10.67228/30715628/ICIoTCAA-2026P105

    Published 08-31-2026

  • Small Language Models, PostgreSQL, Query Optimization, Database Performance, SQL Query Rewriting, Index Recommendation, Query Planner Optimization, AI-Augmented Databases, Healthcare Database Systems, Safe AI Deployment

    Issue

    ICIoTCAA26 - Conference Proceeding

    Section

    Articles

  • Abstract

    Database performance is one of the few problems that never stays solved. As data grows and access patterns shift, queries that were fast become slow, indexes that were right become wrong, and the tuning work begins again. Traditionally this work is manual, expert-intensive, and reactive. I designed an SLM-augmented query optimization pipeline for our PostgreSQL estate a small language model deployed inside our own network that continuously analyzes query workload and recommends rewrites, indexes, and planner hints. This article describes the design, and more importantly, the safety architecture that makes AI in the database layer responsible rather than reckless in a regulated healthcare environment.

  • References

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