Using AI to generate YAML entries for dbt models

After an analytics engineer creates a new dbt model, they frequently skip an important step in their workflows: Generating YAML entries that include the model name, columns, and descriptions. 

It’s easy to see why so many people neglect this step – generating YAML entries for dbt models can be frustrating and time-consuming. When you’re trying to learn the dbt framework, this seemingly small task can be extra onerous. 

The result? Poorly documented work and a variety of downstream data problems. 

Luckily, there’s an easy way to save tons of time by using AI Data Sidekick to automatically generate a YAML entry for a dbt SQL query.

Automatically write SQL in dbt Cloud using AI

Data Sidekick combines the power of AI with context from your data warehouse to automate common data-related tasks. 

With the dbt Config data app, you can input a SQL query and instantly: 

• Generate a dbt YAML file entry for the model that the SQL query creates
• Automatically define the model’s name, columns, and definitions
• Add tests on fields to double check that everything is working correctly

See how Data Sidekick performs in the wild

Curious about how Sidekick performs in the wild? See what Kyle Dempsey, Head of CX and Solutions Architecture at AirOps, has to say about using it to generate YAML entries for his dbt data models.