The goal of Text-to-SQL is to let users query databases with natural language, yet real‑world deployment encounters three major hurdles: large and obscure schemas, difficulty retrieving relevant tables and columns due to structured schemas, and SQL outputs that lack syntactic or semantic validation. Reflect‑SQL tackles these issues with a multi‑stage self‑reflection pipeline. It first builds a knowledge base to grasp obscure schemas; then a retrieval loop uses an LLM‑as‑judge to rewrite the user query and select the most pertinent tables and columns; next a synthesis loop lets the LLM generate SQL, which is scored and automatically corrected by a judging model; finally an entailment loop continuously refines the whole process and feeds new insights back into the knowledge base. Each stage forms a feedback‑driven closed loop, improving both syntax and semantics of the generated SQL. On the BIRD benchmark the framework reaches 72.03% execution accuracy, markedly surpassing state‑of‑the‑art baselines and demonstrating a significant reliability boost for enterprise use.
Review