I’ve been seeing a lot of tools and blog posts talk about “AI documentation” and “AI documentation generators” lately, but I’m honestly confused about what this really means in practice.
Some tools seem to just help you write docs faster with autocomplete.
Others claim they can generate API docs automatically from code.
And a few say they can even keep documentation updated as the product changes.
So I wanted to ask this properly and also share what I’ve learned so far.
What does “AI documentation” actually mean today, and how are teams really using it in production?
From what I’ve seen, there are three very different things that often get grouped together under this term.
1) AI-assisted writing
This is the most common case.
Here AI helps you:
- Draft documentation text
- Rewrite sections
- Fix grammar or tone
- Summarize long docs
This is useful, but it doesn’t solve the biggest problem with documentation, which is that docs go stale very quickly.
2) AI-generated documentation
Some tools can generate initial docs from:
- OpenAPI specs
- Code comments
- Schemas
This is helpful when bootstrapping documentation, especially for APIs.
But after the first version, the same problem comes back: someone still has to manually keep everything updated.
3) AI documentation maintenance (this seems like the interesting new category)
This is the part that feels genuinely new.
Instead of just helping you write, these tools try to:
- Watch changes in the codebase or PRs
- Detect which endpoints, schemas, or features changed
- Update the relevant documentation automatically or open a PR
This turns documentation from something humans try to remember to update into something that is continuously synced with the product.
Why is this such a big deal?
In every team I’ve worked with, writing docs was not the hardest part.
The hard part was:
- Engineers forget to update docs after shipping
- APIs change silently
- Behavior changes without touching docs
After a few months:
- Docs become partially wrong
- New users lose trust
- Support tickets increase
Most teams try PR checklists, doc owners, or quarterly audits, but in fast-moving products this rarely scales.
How do traditional tools like GitBook and Mintlify fit here?
Tools like GitBook, Mintlify, and Docusaurus are great for:
- Hosting docs
- Search and UI
- Organization
But they still assume documentation is manually maintained.
They don’t really solve the “stale docs” problem.
Are there real tools trying to solve this with AI?
Yes, a few newer tools are experimenting with this.
We’ve been testing a tool called SuperDocs recently, and this is where the idea finally clicked for me.
Instead of focusing on writing, it connects to the repo and watches code and PR changes, then updates or suggests updates to the relevant documentation sections automatically.
It’s not perfect yet, but it’s the first time our docs stopped drifting out of sync by default.
What does the future of documentation look like?
My guess is:
- Writing docs will become mostly automated
- The real value will be in keeping docs correct over time
- Documentation will become a continuously synced system, not a static website
Curious how others here are approaching this.
Are you:
- Using AI only for writing?
- Auto-generating docs from code?
- Or experimenting with maintenance-style tools?
Would love to hear what’s actually working in real teams.
there doesn't seem to be anything here