Calling a table "orders" does not tells an agent whether revenue is gross, net, or after refunds. In most of the cases, text2sql fills the gap by guessing, and when it guesses wrong it returns a confident, well-formatted number instead of an error. A semantic layer defines metrics, dimensions, entities, and joins once in YAML; the agent picks a metric and an engine generates the SQL deterministically. Now a wrong pick surfaces as "I can't answer that" rather than a wrong figure on a board deck. In the 2026 dbt's benchmark Claude Sonnet 4.6 scored 90% on text2sql and 98.2% through the semantic layer, while GPT-5.3 Codex at 84.1% and 100%. But it is not all rainbows and sunshines.
Co pokazuje ta sesja
Sesja porównuje swobodne zgadywanie text-to-SQL z zarządzaną warstwą semantyczną, w której metryki, wymiary, encje i relacje są zdefiniowane, zanim agent wybierze wymagane pojęcie biznesowe.