From Query Plans to Prompts: How AI Understands SQL Server Internals

This session focuses on how modern GenAI systems can be taught to understand SQL Server internals in the same way an experienced DBA or performance engineer would, rather than treating SQL as plain text.
What the session includes:
• How SQL Server query plans (estimated vs actual), wait statistics, and execution metadata can be translated into AI-friendly context.
• Designing prompts that allow AI to reason about:
Join strategies, cardinality estimation issues, missing indexes
Parameter sniffing, plan regression, and common performance anti-patterns
• Live or recorded demos where AI explains why a query is slow, not just what it does.

Technologies involved:

SQL Server (query plans, DMVs, Query Store), Amazon Bedrock (Anthropic Claude models), Optional comparison with non-AWS LLMs for contrastLightweight Python tooling for plan extraction and prompt orchestrationKey takeaway for attendees:
AI is not replacing DBAs — it is learning from their mental models. This session shows how to embed that expertise into AI systems in a practical, explainable way.

1. Understand how AI interprets SQL Server execution plans and optimiser decisions
2. Learn where AI reasoning aligns with — and diverges from — SQL Server internals