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 error-driven hint learning pipeline across Development and Deployment to consolidate debugging traces in
The proliferation of LLMs and the increasing demand for practical, robust database interaction through natural language necessitate improved Text-to-SQL solutions that can handle real-world complexities beyond prototypes.
This development addresses a critical bottleneck in deploying LLMs for enterprise data access, moving Text-to-SQL from academic demonstration to production-grade reliability and efficiency.
The ability to dynamically optimize Text-to-SQL prompts and adapt to evolving schema and user preferences fundamentally improves the practicality and cost-effectiveness of LLM-driven database interfaces without constant manual fine-tuning.
- · Enterprises with complex SQL databases
- · Developers building AI-powered data tools
- · Data analysts and scientists
- · LLM providers with Text-to-SQL offerings
- · Companies relying on rigid, expensive supervised fine-tuning for Text-to-SQL
- · Manual SQL query generation services
- · Basic, unoptimized Text-to-SQL solutions
Automated hint optimization significantly reduces the cost and complexity of deploying LLM-powered data access interfaces in production environments.
Broader adoption of natural language interfaces for databases could democratize data access within organizations and reduce reliance on specialized SQL expertise.
The integration of AI agents capable of self-optimizing database interactions could lead to more autonomous enterprise data management systems and accelerate data-driven automation.
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Read at arXiv cs.AI