2 posts tagged with "Documentation Workflow"

In my last post, How I Review Technical Docs with AI, I mentioned in passing that before bringing Codex into the workflow, I had already built a small local AI system for catching typos, terminology mistakes, basic awkward sentences, and the occasional formatting glitch.

It was a one-line aside, but a few readers wrote in asking for more. So this post zooms in on that piece: why a local AI content review system is worth building in the first place, how it differs from just calling a hosted LLM, and what it takes to go from a working demo to something a team will actually open every day.

Once your docs fully live in GitHub and follow a Docs-as-Code workflow, review starts to look a lot more like software collaboration. That is mostly a good thing: we get history, branches, pull requests, and a cleaner workflow. But it also means the quality bar rises fast.

Over the past few months, I ended up building a layered review workflow for technical content. I started with a lightweight local validation step for typos and surface-level issues, then added Codex for deeper local and cloud review, and finally used AGENTS.md to turn a lot of tacit review judgment into reusable rules.

This post is not just a tool recap. It is really about why this workflow is worth building, where each layer helps, and where AI review still needs a human in the loop.