How to Add AI Answers to Your Documentation Site
A straight-line guide to adding AI answers to a documentation site: what to pick, how to index your content, where to install the widget, and how to test it before announcing it.
Adding AI answers to a documentation site is not the three month project it tends to look like from the outside. It is a half day project if you pick the right tool, and a three month detour if you pick the wrong one.
This guide walks through the version that takes a half day. Five steps, one deliverable, no custom model training, no separate backend to host.
What you need before you start
Three things, all of which most docs teams already have.
Markdown or MDX docs in a repo. The tool has to read something. Markdown is the most common and the best supported. If your docs are in a proprietary format or behind a CMS, the integration work is larger and this guide applies less directly.
A way to serve a script tag on the docs site. Most static site generators (Docusaurus, VitePress, Nextra, Mintlify) let you add a script tag to the site header in a single config option. If your docs framework does not, you can drop it in a layout file manually.
An hour of support team time. Not to configure anything. To produce a list of the top ten to twenty questions your support team sees most often. You will test against that list before the widget goes live.
Skip the list and you will ship an AI that might be answering well or might be inventing things, with no way to tell the difference.
Step 1: Choose a source-grounded tool
The single most important decision. Everything else is easy if this step is right and impossible if it is wrong.
A source-grounded tool reads your docs and only answers from them. It cites the section every answer came from. It says "I could not find this" when the docs do not cover a topic.
A tool that is not source-grounded is a general-purpose LLM with a docs-themed wrapper. It will answer confidently about your product whether it knows anything about it or not. Do not install one of these. No amount of polish on the widget can fix an underlying system that invents content.
Four questions to ask any tool before committing:
- Does every answer include a link to the exact page or section it came from?
- Can I see a log of every question asked, with the content the tool retrieved?
- What does it do when the docs do not cover the question?
- How do I update the index when I change a page?
If the tool dodges any of those, move on.
Step 2: Index your Markdown
Indexing means handing the tool your docs so it can read and chunk them. This usually takes one of two shapes.
CLI push. You run a command from your docs repo that reads the Markdown, chunks it, and uploads the chunks. This is the right model for most teams because it fits naturally into a build or a CI job. When the docs change, you push again.
Repo watcher. Some tools connect to a GitHub repo and re-index on every push. This is lower effort but tends to be noisier, and it adds a network dependency between your docs build and the tool.
For either model, the initial index of a medium sized docs site (500 to 1,000 pages) should take minutes, not hours. If it takes longer, either the tool is doing something expensive per page or the chunking strategy is off.
A good CLI run also gives you a summary: pages indexed, sections detected, chunks created, any pages that were skipped. Read the skipped list. Usually the skips are intentional (drafts, internal pages), but sometimes they are a sign of a parsing bug.
For a concrete example, Knoku's CLI pushes Markdown with one command and returns a summary you can paste into a CI log. Free to try without a credit card at signup.
Step 3: Drop the widget on your site
The widget is the visitor-facing surface. One script tag, one or two config options, done.
What to configure at minimum:
- The project or workspace ID the widget should query against
- The color or theme so the widget matches the docs site
- The default open or closed state (most teams want closed, open on click)
- The position on the page (bottom right is standard and expected)
What should not require configuration:
- Styling that bleeds into the rest of the site (Shadow DOM handles this for you)
- Custom React or Vue wrappers (the widget should be framework agnostic)
- Backend proxies or API routes (the widget talks directly to the tool's API)
If the installation requires any of those, the tool is too heavy. A docs widget is not a microservice. It is a script tag.
Test it in a staging deploy first. Open the docs, click the widget, ask three real questions. Make sure the answers are cited. Make sure the citations link back to the actual pages.
Step 4: Test with the real question list
This is the step most teams skip. Do not skip it.
Take the ten to twenty question list from support. Ask each one to the widget. Score each answer on two dimensions:
- Correct. Does the answer reflect what the docs actually say?
- Cited. Does the answer link to a page where you can verify?
Aim for at least eighty percent of the questions coming back correct and cited before you announce the widget. Anything below that is a setup bug or a docs gap, and it is cheaper to fix now than to roll out the fix later.
For the questions the widget missed, check the logs. Most of the time the retrieval layer found nothing because the docs do not actually have the answer. That is now a docs roadmap, not a tool problem.
Step 5: Announce and measure
Once the test list is passing, turn the widget on for all visitors.
Three things to watch in the first two weeks:
- Volume. How many questions come in per day. Surprisingly often higher than expected.
- Answer rate. Share of questions the widget answered with a cited passage.
- Unanswered list. Every question where the tool said "I could not find this." This is the single most valuable export in the whole system.
The unanswered list is what turns the tool from a widget into a feedback loop. Read it weekly. Add pages for the questions that repeat.
Most docs sites see measurable support impact within two weeks. The pattern is consistent: top questions deflect, the docs gaps become visible, and the team starts writing pages for questions they did not know visitors were asking.
If you want to run through these steps on your own site, start free on Knoku. Push Markdown with the CLI, drop the script tag, and test against your own question list. Pricing scales when you need more messages than the free tier.