Can LLMs Replace the DBA? Evaluating Ontology-Driven Prompting for Relational Database Tuning Tasks - Leão, Eric Ruas; Lifschitz, Sergio; Almeida, Ana Carolina; Haeusler, Edward Hermann (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de DadosDO - 10.5753/sbbd.2026.249216, 2026)
Relational database tuning traditionally relies on specialized heuristics and expert intuition, making the process difficult to scale, audit, and reproduce. While recent research has explored autonomic and AI-based systems to assist Database Administrators, the emergence of Large Language Models introduces a new paradigm for automated tuning. This paper investigates the extent to which LLMs can augment or replace traditional DBA expertise in complex, knowledge-dependent tuning tasks. We evaluate tuning recommendations generated by two distinct LLMs across three model variants each, comparing zero-shot performance against ontology-driven prompting. Using a PostgreSQL environment with a TPC-H-like workload, we measure the power, throughput, and storage footprint of the recommended structures. Our results indicate that expert knowledge—whether from an experienced DBA or a formal ontology—is not merely a source of domain vocabulary. Ultimately, the ontology acts as a critical precision mechanism that codifies the ’rules of thumb’ inherent to expert tuning, effectively grounding the LLM’s generative capacity and optimizing the trade-off between performance and storage footprint.