How to evaluate SQL queries that run successfully but return the wrong answer

Effective text-to-SQL evaluation distinguishes execution success, which only confirms that a query runs without errors, from result correctness, which verifies that returned data matches a reviewed answer to the intended business question. Successfully executed SQL can still contain semantic errors involving filter values, date interpretations, aggregation levels, join fan-out, duplicate rows, ties, nulls, column mappings, and numeric precision. Reliable evaluations therefore require explicit result-comparison policies, including rules for ordering, duplicates, aliases, empty results, and tolerances, as well as reviewed reference answers run against fixed database snapshots and defined reference dates. The guidance recommends tracking execution, result correctness, query similarity, and end-to-end correctness separately, since skipping failed queries can make correctness averages appear to improve while total correct answers decline. Using Braintrust, teams can record generated SQL, results, and errors; attach separate scorers; pin dataset versions; compare candidate runs with baselines; inspect regressions; and enforce quality thresholds in CI, while continuously expanding test fixtures with edge cases and reviewed production failures.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.