Documentation Generation

This guide shows how DinoAI Agent automates the creation of documentation for multiple models simultaneously, saving hours of manual documentation work.

Documentation is often neglected in data projects because it's time-consuming to write descriptions for every model and column. Teams frequently postpone documentation or conduct separate "documentation sprints" that take days to complete.

DinoAI Agent can automatically generate comprehensive documentation for entire folders of models, complete with column descriptions and appropriate tests.

Example Prompt

I have a bunch of new files in my Marts folder. Can you document this for me?

Optional: Add context by selecting a directory containing the models you want to document. Using directory context is especially powerful for documentation tasks as it allows DinoAI to document multiple models at once.

How It Works

After you enter your prompt:

1. DinoAI scans all models in the specified directory

2. It analyzes each model's structure, column names, and relationships

3. It generates schema.yml files with model descriptions, column descriptions, and tests

4. If configured, DinoAI follows your .dinorules documentation standards

Example Output

DinoAI will generate professional quality documentation (.yml files). See example below:

.yml file example

version: 2

models:
  - name: int_f1_race_results_by_constructor
    description: "This model combines race results with constructor information to analyze performance by constructor across different races and seasons."
    columns:
      - name: race_id
        description: "Unique identifier for each race event"
        tests:
          - not_null
          
      - name: year
        description: "The year in which the race took place"
        tests:
          - not_null
          
      - name: race_name
        description: "The official name of the race event"
        
      # Additional columns with descriptions and tests...

Key Benefits

• Comprehensive Coverage: Generates documentation for all models at once

• Consistency: Maintains a uniform documentation style across projects

• Test Integration: Automatically adds appropriate tests based on data types and relationships

• Time Savings: Turns days of documentation work into minutes

• Adoption: Makes it easy to keep documentation up-to-date as models evolve

When to Use This

• Before sharing models with stakeholders

• When preparing for project handovers

• During documentation clean-up efforts

• After creating new models or making significant changes

• When implementing testing strategies