Writing Pull Request Descriptions with AI

Pull request descriptions are one of the most routinely neglected parts of a development workflow. Under deadline pressure, it is easy to type "fixes the bug" and move on. But a well-written PR description saves review time, accelerates approval, and creates a permanent record of why code changed — not just what changed. AI can produce a solid first draft in seconds, given the right input.

What Makes a Great PR Description

A useful PR description covers:

  • Context — what problem does this solve, and why now?
  • What changed — a scannable summary of the work done
  • How to test it — specific steps a reviewer can follow to verify the change works
  • Screenshots or links — essential when UI has changed
  • Breaking changes or migration notes — anything that affects other teams
  • Issue or ticket references — so reviewers can trace back to the original requirement

Getting the Right Input for AI

# High-level narrative of work done
git log main..HEAD --oneline

# Files changed overview
git diff main..HEAD --stat

# Full diff for detailed descriptions
git diff main..HEAD

The --oneline output gives a high-level narrative. The --stat output shows which files were touched. Together they let AI summarise the scope without reading every line.

Writing PR Descriptions with AI Tools

GitHub Copilot

GitHub includes a "Generate with Copilot" button directly on the PR description field at GitHub.com. Click it after pushing your branch and Copilot reads your diff and generates a description automatically. Always edit the output before publishing — the raw draft is a starting point, not a finished product.

In the Terminal with Claude or ChatGPT

# Copy full diff to clipboard (macOS)
git diff main..HEAD | pbcopy

Then send a prompt like:

"Write a GitHub pull request description for this diff. Include: what changed and why, how to test it, and any breaking changes."

Cursor

In the Cursor chat panel, paste your git log --oneline output alongside a prompt asking for a PR description. Cursor's codebase awareness supplements the diff with context about the files involved.

Prompting AI for Different PR Types

Tailor your request to the type of change:

  • Bug fix — "Emphasise the root cause, what was broken, and how this change fixes it."
  • Feature — "Focus on user impact, what is new, and how to test it end-to-end."
  • Refactor — "Explain what was simplified and confirm that behaviour has not changed."
  • Breaking change — "Highlight migration steps prominently at the top."

Using a PR Template

Add .github/pull_request_template.md to your repository and GitHub will pre-populate the description field on every new PR:

## What does this PR do?

## Why are we making this change?

## How to test

## Breaking changes
None / [describe]

## Related issues
Closes #

To use AI with this template, paste both the template and your git diff into your AI tool, then prompt:

"Fill in this PR template based on the diff below. Be concise and specific."

AI fills in templates reliably and consistently — it removes the friction of starting with a blank box and enforces structure without requiring anyone to police it.

Editing the AI Output

AI-generated PR descriptions tend to be verbose. Before publishing, trim:

  • Filler openers ("This PR introduces...", "In this pull request...")
  • Overly detailed technical descriptions that belong in code comments
  • Inflated importance of minor changes

And add back what AI cannot know:

  • Specific issue or ticket numbers
  • Business context behind the decision
  • Test steps accurate to your actual environment

DeployHQ and Your Deployment Workflow

At DeployHQ, you can trigger deployments directly from branches or pull requests, making the PR the natural checkpoint before code reaches production. A clear, well-written PR description means faster approval and a faster path to deployment. When reviewers understand what changed and why, review cycles shrink and deployment confidence goes up.