Answering Natural Language Questions With Dbt

What it does

This skill enables AI agents to interpret and answer natural language questions directly against your data warehouse using dbt. It bridges the gap between human inquiry and raw SQL by automatically generating and executing queries based on conversational input.

When to use it

• Ad-hoc Analysis: Quickly investigate trends or anomalies without writing complex SQL manually.
• Stakeholder Reporting: Generate instant answers for business questions from non-technical team members.
• Exploratory Data Science: Rapidly prototype hypotheses by querying large datasets through simple text prompts.
• Automated Dashboards: Feed dynamic, conversational insights into reporting tools that require real-time data validation.

Key capabilities

• Parses natural language queries into executable SQL statements.
• Leverages dbt models and macros to ensure query accuracy and consistency.
• Executes queries directly against the connected data warehouse.
• Returns structured results based on the specific question asked.

Example prompts

• "Show me the total revenue for each product category last quarter."
• "Which customer segment has the highest churn rate in the past 30 days?"
• "Compare average order values between the US and EU regions for January."

Tips & gotchas

Ensure your dbt models are up to date before asking questions, as the skill relies on the current state of your data definitions. While powerful for standard aggregations, complex logic may require refining your natural language prompt to be more specific about joins or filters.