金仓数据库
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- NL2SQL accuracy stuck at 40%? The author's answer: schema comments, not the model
A practitioner account of a government data-platform deployment where the generated SQL was syntactically valid and ran fine, but filtered on the wrong column: user_status stored registration-channel codes while the real validity flag was is_valid, so "valid users" came out wrong. The author argues deployment failures mostly stem from undocumented fields, undefined business metrics, and unclear ownership, and recommends a three-layer approach: add table/column comments with enumerated values, write machine-readable metric definitions with formulas, time windows, exclusions and an owner, then choose where the semantic layer lives. In one internal validation on more than 100 questions with the same model, executable-SQL rate stayed near full marks while answer accuracy reportedly rose from just over 40% to above 85% after comments and metric definitions were added.
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