text2sql-system-prompt-updater

To fix recurring Text2SQL prompt failures, apply a failure-pattern driven workflow that clusters failing queries by behavior and enforces generalizable, non-benchmark-specific policy rules to update system prompts. This ensures fragment edits address broad failure classes rather than specific test cases.

Preventing SQL generation regressions requires iterative scoped evaluation with regression checks after applying policy-driven prompt edits. You rerun only the failed records from a scoped failing range until the failure categories stabilize, ensuring new rules do not break previously passing SQL generation.

Updating Text2SQL system prompts involves producing concise, durable fragment-based rules that enforce schema grounding, minimality, predicate precision, and aggregation correctness. These policy-first edits are applied to context fragments without embedding dataset literals.

No, you cannot modify the model-under-test when debugging Text2SQL generation errors. Safe guardrails dictate keeping the model constant, avoiding dataset-specific examples, and preferring fragment primitives over persona edits to isolate the impact of prompt policy changes.

You should use fragment primitives instead of persona edits when you need durable, targeted policy rules for SQL generation. Fragment primitives allow precise enforcement of schema grounding and predicate precision without the broad, unpredictable behavioral shifts associated with persona modifications.

Text2SQL prompt updates fail to generalize when they contain dataset-specific examples or one-off fixes instead of broad policy rules. Hardening prompts requires clustering failures by behavior and applying generalizable rules to context fragments to address entire failure classes.