  Large Language Models (LLMs) have made significant progress in assisting
users to query databases in natural language. While LLM-based techniques
provide state-of-the-art results on many standard benchmarks, their performance
significantly drops when applied to large enterprise databases. The reason is
that these databases have a large number of tables with complex relationships
that are challenging for LLMs to reason about. We analyze challenges that LLMs
face in these settings and propose a new solution that combines the power of
LLMs in understanding questions with automated reasoning techniques to handle
complex database constraints. Based on these ideas, we have developed a new
framework that outperforms state-of-the-art techniques in zero-shot text-to-SQL
on complex benchmarks
