Prompt Template-Driven Large Model SQL Generation

When executing text-to-SQL tasks, current large generative models often fail to obtain correct query results due to semantic understanding bias, which requires extensive manually written prompts for correction. This paper proposes a unified prompt template framework integrating three specialized template types (variable-type, recurrent-type, multi-branch-type) with formalized definition and algorithmic logic. By populating these templates with structured database metadata through a rule-based matching mechanism, we achieve rapid, scalable prompt generation that eliminates heavy manual intervention. Experimental results on a custom event management database demonstrate that the proposed method reduces semantic understanding bias, achieving an overall SQL generation accuracy of 87.2% (close to the 90.0% accuracy of manual prompts) while reducing prompt engineering time to 25% of the manual method. The framework’s innovation lies in its integration of programming-like logic (variable binding, loop iteration, conditional branching) into a unified prompt generation paradigm, distinguishing it from traditional string interpolation and rule-based prompt methods.