Designing Text-to-SQL as a Data Pipeline, Not a Single Prompt
Puzzleheaded_Box2842 · reddit · 2026-07-30
The author shares engineering insights on building Text-to-SQL systems, arguing for designing it as a comprehensive data pipeline rather than a simple single-prompt task.
A high-quality Text-to-SQL sample requires more than just a question-to-SQL mapping; it needs schema context, sample values, executable SQL, reasoning traces, and difficulty measurement. The author suggests splitting the workflow into operators: generating/varying SQL, filtering by executability, generating natural language questions, verifying question-SQL correspondence, and building the final prompt.
A core highlight is keeping execution in the loop: generated SQL must be validated against a database, and CoT candidates can be voted on based on whether their SQL produces consistent results, providing a stronger signal than pure LLM judgment. Additionally, separating SQL "structural difficulty" from "execution difficulty" is highly valuable. This design is already built into the open-source project OpenDCAI/DataFlow.
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