The Real-World Trade-Offs of Natural Language to SQL Systems
Natural language to SQL (NL2SQL) systems are one of the most compelling applications of language models—but also among the hardest to get right in production. Turning messy human questions into precise, performant SQL queries touches nearly every challenge in AI: ambiguity, domain context, schema evolution, and validation. Here’s a breakdown of the core lessons learned from real-world implementations—and why building a robust NL2SQL stack requires more than just dropping an LLM behind an input box.
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