TL;DR
- Claude Code generates changelogs by reading git commit history and applying style guidelines
- Reduces changelog creation from 3-4 hours to 15-20 minutes per release (75+ hours saved annually)
- Maintains a changelog-guidelines.md file that Claude references for company voice and structure
- Best for: Product managers and developers shipping software on regular release cycles
- Key lesson: AI-generated changelogs are often more consistent because AI doesn’t get tired by commit 30
A product manager reduced changelog creation from 3 hours to 15 minutes per release by having Claude Code read git history and generate user-facing release notes automatically.
Manik dreaded release day.
Not the technical part. His engineering team handled that smoothly. The part he dreaded came after: writing the changelog.
“Every release meant going through dozens of commits. Reading cryptic commit messages. Figuring out what each change meant for users. Then translating all of it into human language.”
The process took hours. Sometimes an entire afternoon. And it had to be done every two weeks.
The Hidden Time Sink
Changelogs seem simple. They’re just lists of what changed.
But good changelogs require archaeology. Commit messages like “fix bug” or “update styles” don’t help users understand what improved. Someone has to decode each change and explain why it matters.
Manik was that someone.
“I’d open the git log, start scrolling through commits, and my heart would sink. Forty commits. Sixty commits. Each one needing investigation.”
The worst part? By the time he finished, he was mentally exhausted. The creative work — thinking about positioning, user communication, what to highlight — got squeezed out by the mechanical work of compiling information.
The Experiment
Manik heard about Claude Code from a developer friend. “He used it for coding, but he mentioned it could read files and do research. That gave me an idea.”
What if Claude could read the commit history directly?
He tried it. Pointed Claude Code at his repository and asked: “Scan all commits from the last two weeks. Then pull in my changelog guidelines document. Write a user-facing changelog.”
“I expected it to fail or produce garbage. Instead, it understood the codebase context. It grouped related commits together. It translated technical changes into user benefits.”
The first draft wasn’t perfect. But it was 80% there. Manik spent 15 minutes polishing instead of 3 hours creating from scratch.
The Process That Emerged
After several releases, Manik refined his approach.
Before each release:
He’d run a simple prompt: “Look at commits since [last release date]. Reference our changelog-guidelines.md file. Write a draft changelog organized by: New Features, Improvements, Bug Fixes.”
Claude would:
- Parse the commit history
- Cross-reference with any linked issues or PRs
- Apply the company’s style guidelines
- Group changes logically
- Translate technical descriptions into user language
What Manik still did:
Review the draft. Sometimes Claude misunderstood a change’s significance. Sometimes a bug fix deserved more prominent billing because customers had complained about it. The human judgment layer remained essential.
“I went from creating changelogs to editing changelogs. That’s a much better use of my time.”
The Quality Upgrade
Surprisingly, the AI-assisted changelogs were often better than Manik’s manual ones.
“When I did it myself, I’d get tired by commit 30. I’d rush through the last batch. Important changes got buried because I was mentally checked out.”
Claude didn’t get tired. Every commit got the same attention. Patterns emerged that Manik might have missed — related changes scattered across different commits that belonged together in the narrative.
“It also caught things I would have skipped. Like a performance improvement that wasn’t flashy but actually affected every user. I would have written one line. Claude wrote a paragraph explaining the impact.”
The Time Math
Manik tracked the numbers:
Before Claude Code:
- 3-4 hours per release
- Every two weeks
- ~90 hours per year on changelogs alone
After Claude Code:
- 15-20 minutes per release
- Same cadence
- ~13 hours per year
“I got back 75+ hours annually. From one workflow change.”
He reinvested that time into actually reading what users said about features, planning better releases, and occasionally leaving work at a reasonable hour.
The Unexpected Benefits
Beyond time savings, the automation had ripple effects.
Consistency improved. The changelogs followed the same structure every time. New team members could understand the format immediately. The company’s voice stayed coherent across releases.
Historical tracking improved. Because generating changelogs was now easy, Manik started keeping more detailed records. He could answer “when did we ship that feature?” in seconds.
Communication improved. With mental energy freed up, Manik wrote better release announcements. He had time to craft the email, not just compile the list.
The Setup
Manik’s workflow required minimal configuration.
He kept a changelog-guidelines.md file in the repo root. It contained:
- The company’s voice and tone preferences
- Example changelogs for reference
- Rules about what to include vs. exclude
- How to handle breaking changes
When Claude Code ran, it read this file automatically as context. The guidelines evolved over time as Manik refined what worked.
“The file became a living document. When Claude made a mistake, I’d update the guidelines. Next time, the same mistake wouldn’t happen.”
The Broader Pattern
Manik realized changelog generation was just one instance of a larger pattern: tasks that require compiling information from scattered sources.
“Developers hate writing docs because it means reading code and translating. PMs hate status reports because it means aggregating data from five tools. Writers hate research roundups because it means synthesizing many sources.”
All of these shared a structure: gather → analyze → synthesize → write.
Claude Code excelled at all four steps when pointed at the right sources.
“I started looking at my week differently. What else am I doing that’s basically ‘compile information and write about it’? That’s where Claude helps most.”
The Warning
Manik offered caution for others trying this approach.
“Don’t abdicate judgment. The AI doesn’t know what matters to your users. It doesn’t know that one customer screamed about a bug for months. It doesn’t know the political importance of mentioning the CEO’s pet feature.”
Human review remained non-negotiable. The automation handled the mechanical work. The strategic decisions stayed human.
“I review every line before it ships. Sometimes I rewrite whole sections. But I’m starting from something, not from nothing. That makes all the difference.”
The Result
Two years into the workflow, Manik couldn’t imagine going back.
“It’s like discovering spell-check. Could I write without it? Sure. Do I want to? Never.”
The changelogs still bore his name. They still reflected his judgment about what mattered. They just didn’t require him to manually compile information that a computer could compile faster.
“The irony is my changelogs got better after I stopped writing them from scratch. I have more time to think about what to say instead of what to include.”
