answering-natural-language-questions-with-dbt

To translate natural language questions into SQL using dbt, the Skill leverages dbt's semantic layer to map business terms to data models, generating actionable SQL queries for metrics like revenue and engagement.

dbt semantic layer querying uses dbt's defined metrics and relationships to interpret natural language business questions, minimizing ad-hoc SQL customization and ensuring accurate data retrieval.

Yes, you can generate ad-hoc SQL. When the dbt semantic layer results are incomplete, the Skill falls back to constructing and executing raw SQL to answer your data questions.

Model discovery identifies relevant dbt models and their relationships by analyzing artifacts. This constructs accurate queries for operational metrics, handling varying levels of model completeness.

The best way to query revenue metrics from dbt using plain English is providing a natural language question, which the Skill processes via semantic layer guidance or ad-hoc SQL fallback.

Limitations of using dbt semantic layer for natural language queries include insufficient results for complex or unmapped queries, requiring an ad-hoc SQL fallback to fully answer data questions.