Meta:Context: a business context schema in dbt's semantic layer

At dbt Summit 2026, Green Dot’s Keith Binkly presented Meta:Context, a five-layer business-context schema embedded alongside metrics in dbt semantic-layer YAML to help AI agents interpret data beyond producing correct calculations. Developed for Green Dot’s regulated embedded-finance operations, the approach addresses cases where agents misinterpret valid metric comparisons because they lack knowledge of business rules, expected ranges, operational differences, and appropriate actions. Its “DRIED” categories—descriptions, expectations, investigation, relationships, and decisions—provide typed, reviewable fields that are version-controlled and returned with metric values through the semantic-layer API. In testing, context cards helped agents identify likely causes, distinguish unsupported claims, and propose verification queries, while multiple AI models could not reproduce insights originating from an analyst’s real investigation. Green Dot is piloting conversational BI using the schema, which has also attracted interest from regulated organizations, and Binkly recommends starting with a single KPI documented with its purpose, healthy range, and business rules.

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