Set up weekly changelogs and fix docs from GitHub issues
How we’re using Devin to help with changelogs, PR reviews, and large-scale edits across our documentation. Task: How we’re using Devin to help with changelogs, PR reviews, and large-scale edits across our documentation Subscribe to our changelog Receive only our latest updates. No spam, guaranteed. At Neon, our docs team does a little bit of everything. We work on technical documentation, sometimes UI copy, changelogs, reviews, and the occasional regex-heavy cleanup across hundreds of pages. It’s a lot of small, steady work – often, exactly the kind of work you wish an AI agent could just take off your plate. That’s what got us curious about Devin. Unlike other tools we tried, Devin doesn’t just suggest code or text inline; you give it instructions, and it opens a full pull request on your behalf. It’s designed to work across large codebases, operate autonomously, and take care of actual workflows. That sounded promising for documentation work, which often involves well-defined, repetitive changes or coordination across multiple files. We didn’t expect Devin to write our docs for us, but we wondered if it could become a kind of assistant. We’ve been trying it out: here’s what’s working so far. Docs Automation with Devin: What’s Working So Far Simple tasks, automated fast Some of Devin’s biggest wins in our experience come from saving us time on small, repetitive work. For example: we use a Devin Playbook every week to set up our changelog file. It’s not a complicated task, but it used to be a manual one; now we just say, “Set up a changelog for this Friday,” and Devin gets it started. We’ve standardized the process using a playbook. We also use Devin often for addressing GitHub Issues and PRs. We can point Devin to an issue, tell it to review the code and find the relevant parts of the docs to update, and it’ll create or revise a PR with the changes. That’s helped us work through a backlog of older docs issues without needing to manually audit every one. Writing docs with context awareness One of our favorite moments with Devin was when we asked it to document a small new feature. We expected a basic write-up, but when we opened the PR, it included a screenshot (!) At first, we thought it was a placeholder. But Devin had actually found a relevant image elsewhere in the repo (a .png that matched the UI being described) and inserted it appropriately. There was nothing in our prompt that mentioned the image, and nothing in the surrounding text that made it an obvious fit. Our guess is that Devin used OCR or some kind of visual recognition to infer the match. Either way, it was a genuinely helpful addition, and a great little glimpse of how powerful context-aware agents will become. Multi-file edits without the regex headache One of the most annoying aspects of documentation work is when a feature name or term changes. You then need to find and update dozens of references across the docs, without accidentally breaking context or formatting. Traditionally, this meant writing cautious regex passes and still doing lots of cleanup. Naturally, we tried Devin to see how it did here. When we renamed a feature from “history retention” and “point-in-time recovery” to the simpler term Instant Restore, we asked Devin to help. The results were mixed. Devin did find nearly every place in the docset where the old terms were used, something that would have taken a lot of manual effort – that was a win. But its edits leaned heavily on direct find/replace, which led to some awkward constructions like “instant instant restore.” It took another 20+ manual commits to polish the changes. Even if not perfect, having Devin surface all the right places to edit was a huge time-saver. This is exactly the kind of clerical work we’d love to hand off to an AI agent, and Devin’s close. Explaining code when we need it As a docs team, we often have to understand implementation details in the codebase. Devin has been genuinely helpful here. Practical example: recently, our Support team asked for details on the usage alerts customers see when approaching plan limits. After some recent changes, we weren’t sure how those alerts were triggered behind the scenes – and instead of digging through the code ourselves or asking an engineer, we asked Devin. It traced the logic and explained how the alerts worked, saving us a chunk of time and Slack messages.; tools: Devin. Human role: The original post does not say how a person stayed involved.; limitations: Devin operates as a black box, often failing silently on ambiguous tasks without asking for clarification. It struggles with open-ended or complex logic, performing best on well-scoped, repetitive work. The lack of real-time interactivity means users cannot steer the agent mid-task, requiring post-hoc review and correction..
Tools
Published on Agent Work Atlas:
Overview
How we’re using Devin to help with changelogs, PR reviews, and large-scale edits across our documentation
Workflow
How we’re using Devin to help with changelogs, PR reviews, and large-scale edits across our documentation Subscribe to our changelog Receive only our latest updates. No spam, guaranteed. At Neon, our docs team does a little bit of everything. We work on technical documentation, sometimes UI copy, changelogs, reviews, and the occasional regex-heavy cleanup across hundreds of pages. It’s a lot of small, steady work – often, exactly the kind of work you wish an AI agent could just take off your plate. That’s what got us curious about Devin. Unlike other tools we tried, Devin doesn’t just suggest code or text inline; you give it instructions, and it opens a full pull request on your behalf. It’s designed to work across large codebases, operate autonomously, and take care of actual workflows. That sounded promising for documentation work, which often involves well-defined, repetitive changes or coordination across multiple files. We didn’t expect Devin to write our docs for us, but we wondered if it could become a kind of assistant. We’ve been trying it out: here’s what’s working so far. Docs Automation with Devin: What’s Working So Far Simple tasks, automated fast Some of Devin’s biggest wins in our experience come from saving us time on small, repetitive work. For example: we use a Devin Playbook every week to set up our changelog file. It’s not a complicated task, but it used to be a manual one; now we just say, “Set up a changelog for this Friday,” and Devin gets it started. We’ve standardized the process using a playbook. We also use Devin often for addressing GitHub Issues and PRs. We can point Devin to an issue, tell it to review the code and find the relevant parts of the docs to update, and it’ll create or revise a PR with the changes. That’s helped us work through a backlog of older docs issues without needing to manually audit every one. Writing docs with context awareness One of our favorite moments with Devin was when we asked it to document a small new feature. We expected a basic write-up, but when we opened the PR, it included a screenshot (!) At first, we thought it was a placeholder. But Devin had actually found a relevant image elsewhere in the repo (a .png that matched the UI being described) and inserted it appropriately. There was nothing in our prompt that mentioned the image, and nothing in the surrounding text that made it an obvious fit. Our guess is that Devin used OCR or some kind of visual recognition to infer the match. Either way, it was a genuinely helpful addition, and a great little glimpse of how powerful context-aware agents will become. Multi-file edits without the regex headache One of the most annoying aspects of documentation work is when a feature name or term changes. You then need to find and update dozens of references across the docs, without accidentally breaking context or formatting. Traditionally, this meant writing cautious regex passes and still doing lots of cleanup. Naturally, we tried Devin to see how it did here. When we renamed a feature from “history retention” and “point-in-time recovery” to the simpler term Instant Restore, we asked Devin to help. The results were mixed. Devin did find nearly every place in the docset where the old terms were used, something that would have taken a lot of manual effort – that was a win. But its edits leaned heavily on direct find/replace, which led to some awkward constructions like “instant instant restore.” It took another 20+ manual commits to polish the changes. Even if not perfect, having Devin surface all the right places to edit was a huge time-saver. This is exactly the kind of clerical work we’d love to hand off to an AI agent, and Devin’s close. Explaining code when we need it As a docs team, we often have to understand implementation details in the codebase. Devin has been genuinely helpful here. Practical example: recently, our Support team asked for details on the usage alerts customers see when approaching plan limits. After some recent changes, we weren’t sure how those alerts were triggered behind the scenes – and instead of digging through the code ourselves or asking an engineer, we asked Devin. It traced the logic and explained how the alerts worked, saving us a chunk of time and Slack messages.
Reported result
Devin successfully identified nearly all instances of old terms during a rename, saving significant manual search effort, though it required 20+ manual commits to fix context errors. It also autonomously inserted a relevant screenshot into a PR, demonstrating strong context awareness. However, it failed silently on complex, under-defined tasks like ingesting external Markdown files.
Human role
The original post does not say how a person stayed involved.
Limitations
Devin operates as a black box, often failing silently on ambiguous tasks without asking for clarification. It struggles with open-ended or complex logic, performing best on well-scoped, repetitive work. The lack of real-time interactivity means users cannot steer the agent mid-task, requiring post-hoc review and correction.
Try it
Act as a technical documentation engineer using Devin. My goal is to automate a weekly changelog update and fix a specific GitHub issue. Inputs: - Repo URL: [replaceable_repo_url] - Changelog Template: [replaceable_template_path] - GitHub Issue ID: [replaceable_issue_id] - Target Date: [replaceable_date] Steps: 1. Create a Devin Playbook named 'Weekly Changelog Setup' that reads the template and inserts the target date. 2. Run the playbook to generate a draft PR for the changelog. 3. Assign Devin to the GitHub Issue, instructing it to review the code, identify affected docs, and create a PR with updates. 4. Review both PRs manually. Check for 'find/replace' errors in terminology and ensure context is preserved. 5. If Devin fails to open a PR or produces broken links, stop and report the error log. Deliverables: Two PR links (changelog and issue fix) and a summary of manual edits required. Success Metric: PRs are created automatically; manual edits are limited to <5 lines of code per PR. Safeguard: Do not merge any PR without human approval. If Devin does not respond within 10 minutes, cancel the task.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.