3 posts tagged with "AI"

Summary: Large AI models can quickly generate product documentation that looks complete. But whether that content matches real user paths, has been verified, and is safe to publish for external users is still a high-certainty problem. This post records a real documentation engineering practice: how we moved from one-off AI chat generation plus manual patching to a verifiable and reusable documentation production flow built around Rules, Skills, validation Harnesses, and human review. Through this workflow, the documentation team improved the customer-facing quality of complex software docs and moved its focus further upstream, from content writing to documentation platform engineering, knowledge architecture design, and developer experience.

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.