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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.

Hello, I'm Walter Gui, a technical writer. Welcome to my blog, Flowing Docs.

Actually, I've wanted to write a blog for a long time, but I kept putting it off. This time, I finally made up my mind to write an article first and set a flag, so I won't give up halfway. For me, writing a blog is about finding a place to organize scattered thoughts and share them. If this content is helpful to you, that would be the best encouragement for me.

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Many tech people have set up blogs, but stopped after a few posts. I hope I can stick with it.