TAHOE: Text-to-SQL with Automated Hint Optimization from Experience
Large Language Models (LLMs) have democratized database access through Text-to-SQL, but moving from prototypes to production remains difficult. Real deployments must handle strict SQL dialects, massive schemas, and evolving user preferences, while supervised fine-tuning is costly and rigid and agentic test-time scaling is expensive. We present Tahoe, a system that treats prompt optimization as a dynamic data management problem. Tahoe uses an error-driven hint learning pipeline across Development and Deployment to consolidate debugging traces into a structured Hint Bank. Compiler feedback is distilled into reusable Syntax Hints for dialect-specific rules, while execution and user feedback ...
| Authors | Zhiyi Chen, Jie Song, Peng Li |
|---|---|
| Primary category | cs.DB |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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