Using Your Project as Context to Set Up .dinorules

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Example Prompt

Analyze my selected dbt™ project files and create a .dinorules that capture my existing SQL formatting, naming conventions, folder structure, and documentation patterns.

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How to use

1. Open DinoAI by clicking the DinoAI icon (🪄) in the right panel
2. Enter prompt - You can use the above example or create your own
3. Add files as context using the ”@” icon in the DinoAI panel. Select representative files from different folders.
   1. Examples: staging models, intermediate models, marts models, your dbt_project.yml, and schema.yml files

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How It Works

1. DinoAI analyzes the structure and patterns in your selected files
2. It identifies consistent naming conventions, SQL formatting styles, and organizational patterns
3. It examines your folder structure and materialization strategies
4. It creates a .dinorules file that codifies these discovered patterns
5. The generated rules ensure future AI-generated code matches your established standards

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Example Output

Project Context
- Domain: E-commerce analytics
- Data Stack: dbt™ with Snowflake
- Project Structure: source → staging → intermediate → marts hierarchy

SQL Formatting Standards (Based on Analyzed Files)
- Keywords: Always uppercase (SELECT, FROM, WHERE, etc.)
- Indentation: 4 spaces, no tabs
- Comma Style: Trailing commas in SELECT statements
- Aliasing: Always alias tables (use 'u' for users, 'o' for orders)
- CTEs: Prefer CTEs over subqueries for readability
- Comments: Add comments for complex business logic

Naming Conventions (Extracted from Current Models)
- Models: snake_case with prefixes (stg_, int_, dim_, fct_)
- Columns: snake_case, descriptive names
- Tables: Singular nouns (user, not users)
- Primary Keys: Always end with '_id' (user_id, order_id)

Folder Structure and Materializations
- staging/: Views only, basic transformations
- intermediate/: Views, complex business logic
- marts/: Tables or incremental, final business models
- All models tagged by domain and layer

Documentation Requirements
- All models require descriptions
- Business-critical columns need descriptions
- Primary keys need unique and not_null tests
- Foreign keys need relationships tests

Business Logic Patterns
- Always use UTC for timestamps
- Standardize null handling with COALESCE
- Use consistent date formatting (YYYY-MM-DD)
- Apply consistent rounding rules (2 decimal places for currency)

Quality Standards
- All models must compile without warnings
- Use consistent test patterns across similar model types
- Maintain referential integrity through proper testing

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Key Benefits

• Consistency: Ensures all future AI-generated code matches your existing standards
• Discovery: Reveals patterns you may not have consciously documented
• Team Alignment: Codifies implicit knowledge that experienced team members understand
• Efficiency: Creates comprehensive rules without starting from scratch
• Accuracy: Based on your actual codebase rather than generic best practices

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When to Use This

• When setting up .dinorules for the first time
• When your project has evolved organically and you want to formalize conventions
• Before major refactoring efforts to ensure consistency
• When preparing to scale your team and need documented standards