AI tools are everywhere in documentation conversations right now, but most of the discussion stays abstract. This is a concrete account of how I integrated Claude into MinIO’s documentation workflow:
- what I used it for.
- how I structured that use.
- what it measurably changed about how the team worked.
The Starting Point
Technical documentation for a product like MinIO lives close to the codebase. Keeping it accurate means tracking software releases, triaging GitHub issues, auditing existing content, and staying current with a fast-moving engineering team. All while writing new content and updating existing content. The surface area is large and the feedback loops are long.
The question wasn’t whether AI could help. It was whether it could help in ways that were reliable enough to trust and repeatable enough to scale.
What I Used Claude For
Codebase Exploration
One of the hardest parts of documenting developer software is understanding what the code actually does. I used Claude to deep-dive into the MinIO source repositories, explore unfamiliar areas of the codebase, and surface what I needed to write accurately about a feature before drafting anything.
This wasn’t about generating documentation from code. It was about compressing the research phase and getting to a working understanding of complex technical territory faster than I could on my own.
Release Tag Auditing
Every release required comparing the engineering repository’s release tags against the documentation repository to identify what had changed and what needed to be documented. I built a repeatable Claude workflow to do this comparison, identify gaps, and generate draft GitHub issues for the documentation work required.
Before Claude, this was a manual, error-prone, and time-consuming process. Though I would spend a full day reviewing the PRs in a release tag, I might still find out months later that a big fix I had passed over in my review actually contained a critical update that needed to be documented.
Once I pointed Claude to the work, it took over the tedious task of comparing release tags and generating draft issues. It picked up on engineering labels like “Docs Needed” but also reviewed PR titles and descriptions to understand whether a PR without the label still required documentation. In just a few minutes, Claude could process a release tag and output a list of issues that required documentation. With a few more keystrokes on my side, Claude could then create the actual GitHub issue for the release and link it to the upstream issues.
What had been a manual, error-prone process became a structured, consistent one.
CLAUDE.md and STYLE.md
I created both a CLAUDE.md and a STYLE.md to govern how Claude interacted with the documentation repository.
The CLAUDE.md established context about the project structure, toolchain, and conventions so Claude could operate with accurate assumptions.
The STYLE.md captured MinIO’s voice, terminology, and formatting standards.
Together, these meant that any Claude session working on MinIO docs started from a shared foundation rather than from scratch — and that team members using Claude got consistent results.
Reusable Skills for Repeated Tasks
For tasks I performed repeatedly, like the release tag workflow, I created Claude skills that codified the process. Rather than re-explaining the task each session, the skill carried the context, structure, and steps.
This had a compounding effect: as I refined a skill, every future use of it got better. It also meant the workflow was transferable to other team members without requiring them to reverse-engineer my process.
Documentation Consistency Review
I used Claude to audit existing documentation for consistency against the style guide. Claude could quickly flag terminology drift, formatting inconsistencies, and tone mismatches at a scale that would have taken weeks to do manually.
In a doc set of more than 700 pages managed by a team of four writers, creating a cohesive voice that sounded like “MinIO” on every page was a challenge. Claude helped ensure consistency across the board.
Hugo Template Development
Two features we wanted to try on on the docs site didn’t exist in the template. Rather than waiting on design and engineering cycles to come up with the updates, I pointed Claude to them and achieved a working prototype that design could then tweak rather than develop from the ground up.
Feedback widget
I worked with Claude to design and implement a feedback widget directly in the Hugo template, including the front-end HTML, CSS, and JavaScript, as well as a Google Apps Script backend that captured responses into a Google Sheet for tracking. The result was a lightweight, zero-dependency feedback loop that gave us visibility into which pages users found unhelpful. The Google Sheet tracked the URL and heading the user was on when they submitted feedback. The widget allowed the user to add their own comments and even an optional email address for follow-up.
Accessibility fix for cookie overlay
The site’s cookie preference overlay was blocking keyboard users from navigating the page, a real accessibility failure that impacted a member of the docs team. I used Claude to identify the issue, work through the fix, and implement changes to the template that restored full keyboard navigation.
Along with the initial navigation fix, Claude helped us audit and update other accessibility issues across the theme.
Information Architecture Overhaul
The most ambitious project was a complete restructuring of the documentation information architecture. This involved a ground-up renovation of how content was organized, named, and navigated across the doc sites. I used Claude throughout this project to evaluate structural options, pressure-test navigation logic, and draft the new architecture.
The project was in review at the time of my layoff. This was an opportunity to completely redesign the doc site and tackle some long-standing issues. With 700 pages to restructure, move, combine, and rewrite, Claude proved an invaluable assistant.
In just under a month, the docs team had completed the overhaul and had it ready to present to leadership for feedback or implementation. Unfortunately, I was not able to see the final product before my layoff.
What AI Changed
The clearest measure: pull requests went from first draft to review-ready an average of three days faster after these workflows were in place. The whole team benefited from Claude’s insights and guidance, making the human review process more efficient.
But the less quantifiable change mattered too. Working with Claude allowed me to operate across a wider surface area than a single writer realistically could:
- deeper codebase research.
- more consistent audits.
- faster release cycles.
And all without sacrificing accuracy or quality.
While some team members may have been skeptical at first, Claude’s ability to understand and generate high-quality content helped build trust and adoption across the team. When demands on the writing team increased, Claude’s scalability and consistency allowed us to meet those demands without compromising quality, even tackling a huge project like the information architecture redesign in weeks rather than months.
What I’d Tell Someone Starting Out
If you’re a technical writer considering AI integration with your own workflows, here are some things to keep in mind:
- Use a company-approved tool. We had company-wide adoption of Claude, so I was able to rely on a consistent, high-quality AI partner across the team. However, there are real intellectual property and security concerns to consider when integrating AI into your workflows.
- LLMs and other AI tools can be powerful, but they’re not a replacement for human judgment. Always review AI-generated content carefully and make sure it aligns with your organization’s product and style guide.
- Don’t be afraid of the learning curve. All LLMs have a learning curve, but with practice you’ll become proficient at using them effectively.
- The better your prompts, the better your results. Spend time crafting clear, concise prompts that guide the AI toward the output you want.
- Dive into the advanced features of your AI tool to unlock its full potential.