answering-natural-language-questions-with-dbt

Query dbt data models using natural language by leveraging the dbt semantic layer to translate plain English business questions into direct metric queries. This allows non-technical users to retrieve data-driven answers without writing SQL.

You can generate and execute SQL directly from a dbt semantic layer query when the semantic layer is insufficient. The system achieves this by modifying compiled SQL or discovering models to retrieve the required business data.

A dbt semantic layer is not strictly required to answer business questions with natural language. You can also use model discovery tools or analyze dbt project artifacts to generate SQL and retrieve data.

The best way to find total sales last quarter without writing SQL in dbt is to ask a natural language question through the semantic layer. The system translates the plain English query into a direct metric retrieval.

Dbt model discovery for natural language data querying works by analyzing the dbt manifest to locate relevant tables and metrics. This allows the system to generate and execute targeted SQL when direct semantic layer querying is insufficient.