How AI Turns Your GitHub Commits Into Beautiful Release Notes

The translation problem

Every engineering team has two parallel narratives about the same work.

The first is the internal narrative: git commits, pull requests, Jira tickets, Slack threads. This record is detailed, technical, and written for engineers.

The second is the external narrative: what changed, why it matters, what users should do differently. This record is usually absent, outdated, or written in the same technical language as the internal one.

The gap between these two records is where user trust erodes. Users see a product that seems to change unpredictably, with no explanation. Engineers see a changelog that is always behind because nobody has time to write it.

AI closes this gap.

What the AI workflow looks like

A modern AI-powered changelog workflow looks like this:

Step 1: Connect your repository

Link your GitHub, GitLab, or Bitbucket repository to your changelog tool. ShipLog handles OAuth authentication and immediately gives you access to your commit history.

Step 2: Select your commit range

When you are ready to publish a new changelog entry, select the range of commits that make up this release. This could be everything merged since your last tag, a specific pull request, or a date range.

Step 3: AI generates the draft

The AI reads the commit messages, pull request descriptions, and code diff summaries. It produces a structured draft with:

• A short headline that describes the release from the user's perspective
• Categorized bullet points (Features, Bug Fixes, Improvements)
• Plain English descriptions that non-engineers can understand

Step 4: Review and publish

The draft is rarely perfect, but it is usually 80-90% of the way there. You make minor edits, adjust tone, add context for major changes, and publish.

The entire process takes a few minutes rather than the 20-30 minutes a manual changelog entry typically requires.

Why AI is good at this specific task

Changelog writing is a translation task, and translation is something modern LLMs handle exceptionally well.

The inputs are structured (commit messages follow predictable formats), the outputs have a known structure (headline, bullets, categories), and the domain is well-represented in training data (virtually every open-source project has a CHANGELOG.md).

AI models like Claude and GPT-4o have seen millions of examples of both git history and well-written release notes. Given a commit range, they can reliably infer which changes are user-visible, which are internal, and how to phrase the former in accessible language.

What the AI still cannot do

AI-generated changelog entries have real limitations:

• Context about intent: The AI knows what changed, not why you made the strategic decision to change it. Adding the "why" often requires a human touch.
• Impact that is not in the code: If you did a large customer interview study that drove a redesign, that context is not in your commits.
• Suppressing noise: Not all commits should appear in a public changelog. Internal refactors, dependency bumps, and test changes usually do not belong. A good AI tool learns to filter these, but you should review.

Setting up good commit hygiene

The better your commit messages, the better the AI output. A few practices help:

• Use conventional commit format (`feat:`, `fix:`, `chore:`, `docs:`)
• Write commit messages that describe user impact when relevant
• Add PR descriptions for non-trivial changes

Teams that adopt these practices find that the AI-generated drafts require significantly less editing.

Summary

• AI changelog tools bridge the gap between your git history and your users
• The workflow: connect repo, select commits, review AI draft, publish
• AI handles the translation from technical to user-facing language in seconds
• Good commit hygiene improves AI output quality
• The goal is not perfect automation — it is getting 80% of the way there in a fraction of the time