TAHOE: Text-to-SQL with Automated Hint Optimization from Experience

📰 ArXiv cs.AI

Learn how TAHOE optimizes Text-to-SQL with automated hint optimization from experience, improving database access with Large Language Models

advanced Published 11 Jun 2026

Action Steps

Implement TAHOE's dynamic data management approach to optimize prompt optimization
Use Large Language Models (LLMs) to generate Text-to-SQL queries
Configure TAHOE to handle strict SQL dialects and massive schemas
Test TAHOE's performance on evolving user preferences
Apply TAHOE's automated hint optimization to improve query accuracy

Who Needs to Know This

Data scientists and software engineers working on Text-to-SQL systems can benefit from TAHOE's automated hint optimization, improving the efficiency and accuracy of database queries

Key Insight

💡 TAHOE treats prompt optimization as a dynamic data management problem, improving Text-to-SQL performance with Large Language Models

Key Takeaways

Learn how TAHOE optimizes Text-to-SQL with automated hint optimization from experience, improving database access with Large Language Models

Full Article

Title: TAHOE: Text-to-SQL with Automated Hint Optimization from Experience

Abstract:
arXiv:2606.12387v1 Announce Type: cross Abstract: 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 erro

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