AI is changing the documentation landscape fast. More teams are using AI agents to automatically update their docs when product changes ship. And that’s a big step forward.

Because product accuracy has always mattered — and with AI tools summarizing and citing documentation at scale, it’s never been so critical. At GitBook, we’ve seen page views from AI tools increase 5x year-on-year this January. Which shows users aren’t just reading documentation — they’re asking AI to interpret it for them.

But here’s the problem: keeping docs aligned with product updates is only half the job.

If your documentation doesn’t answer the questions users are actually asking, they’ll still open support tickets. Or they’ll turn to AI tools that fill in the gaps with third-party knowledge — or guesswork.

Docs are more than a manual

Documentation has to be more than just a technical manual explaining the functions of your product. Because it’s a core part of your overall product experience.

And when your support team answers the same question 30–50 times a week, that’s a signal that knowledge shouldn’t live in inboxes or ticketing systems. It should live in your documentation.

This is where great AI support documentation becomes essential. As more users rely on AI for answers, those systems will pull from whatever sources they find. Would you rather they cite a four-year-old Reddit thread — or your accurate, up-to-date docs that reflect real user questions?

To succeed, teams need documentation that evolves with product changes and continuously captures user intent.

How we approach AI documentation at GitBook

At GitBook, we’ve built that feedback loop directly into our workflow.

Our Help Center captures common FAQs to power both our AI Assistant and Intercom’s AI bot, Fin. Any unresolved support conversations feed into GitBook Agent, which analyzes them and identifies recurring issues.

The Agent groups related questions into themes and opens suggested change requests based on those answers our support team provided. Instead of manually spotting patterns, it offers proactive recommendations to improve the docs.

Today, Fin resolves almost 60% of support conversations without a human involved. And that number is going up as the Agent fills more knowledge gaps.

All of this works because our documentation reflects both what we ship and what users need.

Modern AI documentation tools shouldn’t just automate release notes — they should connect product, support, and documentation into a single workflow.

If you’re working on custom-built docs platform, integrating this kind of intelligence takes significant engineering effort. An AI-native documentation platform like GitBook includes it out of the box.

How and why to optimize your docs for AI

Documentation’s influence now goes beyond support and onboarding — your docs also impact sales, and marketing. Increasingly, prospective customers ask AI tools about your product before they ever speak to your team.

That’s where GEO — generative engine optimization — comes in. When you structure your content so AI systems can interpret and cite it accurately, you gain a real advantage. Read our documentation GEO guide to explore this in more depth.

And of course, with GitBook you get AI documentation optimization features like llms.txt and MCP built into the platform, so your docs work for humans and machines alike.

Documentation is part of the product experience

Documentation is no longer a static reference site. It’s a core part of the product experience — and often the first place AI looks for answers.

Keeping it up to date with product changes is critical. But answering real user questions is your key differentiator.

If you want documentation that evolves with your product and your users, try GitBook and see how GitBook Agent helps you turn support conversations into better AI documentation.

→ Get started with GitBook for free

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