SQL

Natural language to SQL query generation evaluates text-to-query fidelity and schema reasoning. This task is particularly relevant for analytics chat assistants and simplified database interfaces where users need to query data using natural language. Models must understand both the intent behind the question and the structure of the underlying database schema.

Duration

Input Tokens

1172

Output Tokens

189

Cost

$0.00

Context

Input

Which students joined this year and already finished a lesson?

Expected output

SELECT DISTINCT u.name FROM users u JOIN completions c ON u.user_id = c.user_id WHERE u.role = 'student' AND u.created_at >= DATE_TRUNC('year', CURRENT_DATE);

Model output

SELECT DISTINCT T1.name FROM users AS T1 INNER JOIN enrollments AS T2 ON T1.user_id = T2.user_id INNER JOIN completions AS T3 ON T1.user_id = T3.user_id WHERE T1.role = 'student' AND EXTRACT(YEAR FROM T1.created_at) = EXTRACT(YEAR FROM CURRENT_DATE)