2 posts tagged with "Docs as Code"

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.

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.