Convert Plain Text Requirements into SQL

When asking AI to generate SQL queries, the most frequent failure is receiving code that immediately throws a "column does not exist" error. Without a provided schema, AI invents common column names like orders.order_date or users.is_active. Because the syntax is flawless, it looks valid at first glance, and the error is only caught after execution.

Providing Context solves this issue. By pasting actual column lists or CREATE TABLE statements, the AI no longer needs to guess identifiers. Specifying the target database dialect is equally crucial; since date functions differ across database engines, this prevents dialect syntax errors.

Incorporating a Clarification step prevents inaccurate figures. A phrase like "last month's revenue" does not clarify whether to filter by order placement date, payment settlement date, or whether to exclude refunds. Without forced clarification, the AI will make an unstated assumption, and metric errors often go unnoticed until reports are delivered. Limiting the prompt to 3 questions prevents endless back-and-forth.

The Format constraint requiring an "Interpretation & Referenced Columns" table serves as a verification mechanism. While verifying raw SQL requires deep query reading skills, a plain-text mapping table allows quick visual cross-checking. Wrapping the schema in """ creates clear data boundaries so embedded schema comments are not misread as instructions.

The AI invents table names like orders and users alongside arbitrary column names like order_date. This will either fail to execute or return incorrect metrics (e.g., counting canceled orders). Without documentation of assumptions, the resulting output cannot be trusted.