AI-Generated Pull Request Descriptions

Writing meaningful pull request descriptions is often an afterthought for developers, leading to vague descriptions like “fix bug” or “update code” that provide no context for reviewers. This creates friction during code reviews and makes it difficult to understand the purpose and impact of changes. DinoAI Agent can automatically generate detailed, professional pull request descriptions by analyzing the differences between your branch and main, creating comprehensive documentation that helps reviewers understand your changes.

How to Use

To generate a pull request description, use DinoAI’s pre-built .dinoprompt, Generate a pull request description.

Open DinoAI by clicking the DinoAI icon (🪄) in the right panel
Access Prompt shortcut by clicking the prompt button (”[”) within the DinoAI panel
Create .dinoprompts file (if needed): If you don’t already have a .dinoprompts file, Paradime will offer to create one for you
Select pre-built prompt “Generate a pull request description”

Note: This prompt is pre-configured in your .dinoprompts file and can be updated per your team’s standards. The prompt automatically uses {{ git_diff_dinoai_test }} to include all changes between your current branch and main.

How It Works

Automatic Diff Detection: DinoAI automatically passes the diff between your current development branch and main branch as context ( {{ git_diff_dinoai_test }}- you don’t need to manually specify what changes to analyze
File Analysis: DinoAI analyzes all modified files, added code, and removed code to understand the scope of changes
Intelligent Generation: DinoAI generates pull request descriptions that include summaries, detailed change lists, and relevant context for reviewers

Example Output

DinoAI generates professional pull request descriptions like this:

## Summary
Fixed invalid column identifier causing runtime errors in race analysis models and updated column naming for consistency.

## Changes Made
- Corrected `race_id` column reference in intermediate race results model
- Renamed `driver_name` to `driver_full_name` for clarity
- Updated `championship_position` to `championship_ranking_position`
- Modified `circuit_reference` field naming for consistency
- Resolved merge conflicts with concurrent changes

## Files Modified
- `models/intermediate/int_race_results.sql`
- `models/staging/stg_drivers.sql`
- `models/marts/mart_championship_standings.sql`

## Testing
- All models compile successfully
- Runtime errors resolved
- Column naming follows established conventions

## Impact
- Fixes production pipeline failures
- Improves data model readability
- Maintains consistency with existing naming patterns

Key Benefits

Better Code Reviews: Gives reviewers clear understanding of changes and their impact
Time Savings: Eliminates the manual effort of writing detailed PR descriptions
Consistent Quality: Maintains professional standards across all pull requests
Better Code Reviews: Gives reviewers clear understanding of changes and their impact
Improved Searchability: Detailed descriptions make it easier to find specific changes later

When to Use This

Before creating pull requests for any code changes
When you need to document complex changes involving multiple files
When working with team members who need context about your modifications
For maintaining audit trails and change documentation
Any time you want to improve the quality of your code review process

​How to Use

​How It Works

​Example Output

​Key Benefits

​When to Use This

How to Use

How It Works

Example Output

Key Benefits

When to Use This