Should You Let AI Write Your Documentation?

By the time you finish you will have a defensible answer for your own situation, and if the answer is yes, a division of labour that stops the obvious failure. The framing that makes the decision tractable is this: writing a draft and establishing what is true are two separate jobs. Generation made the first one cheap and did nothing to the second, so the whole question is whether you have the capacity to verify what gets produced.

Before you start

Look at what happens to documentation in your team today. If articles currently go unwritten because nobody has time, generation addresses a real bottleneck. If they go unwritten because nobody knows what changed, generation from a prompt will produce confident text about a product nobody checked, which is a different and worse problem than having no article.

Steps

1. Establish what the model can actually see.
   A model writing from a feature name and a paragraph of context is guessing at specifics: field labels, defaults, error strings. One that reads the diff, the code or a recording of the flow is describing something. This distinction predicts output quality better than the choice of model does.

2. Run a controlled trial on articles you can already grade.
   Pick three existing articles you trust, generate them fresh, and compare line by line. You are looking for invented specifics, not for style. Any confident sentence about a field or default that does not exist is the failure mode you are testing for.

3. Decide the review gate before generating anything at volume.
   Who signs off, against what source, and how long it takes per article. An unanswered review question at this stage becomes an unreviewed publish queue later.

4. Draw the never-unreviewed line explicitly.
   Security, billing, data deletion, migration steps, compliance claims and anything with legal exposure get human verification every time, regardless of how good the drafts look.

5. Give the model the same constraints you would give a new writer.
   House style, article structure, terminology, the audience, what to leave out. Vague instructions produce competent generic text, which is exactly the output that passes a skim review and fails a reader.

6. Publish in small batches and watch what happens.
   Ten articles, then read the feedback and the search behaviour before generating another hundred. A large batch published at once is unreviewable in practice and creates a corpus nobody can vouch for.

7. Track your correction rate.
   Record how much of each draft survives review. If reviewers are rewriting more than they accept, the generation step is costing time rather than saving it, and the honest conclusion is to stop.

8. Plan for staleness the same way you would for hand-written pages.
   Generated articles age at exactly the rate the product changes. Volume makes this worse, because a hundred articles nobody wrote is a hundred articles nobody feels responsible for.

Common mistakes

Judging the draft on how it reads. Fluency is the one thing these systems reliably deliver, and it is uncorrelated with accuracy. The characteristic failure is a well-structured, confidently phrased paragraph describing a button that does not exist.

Generating from a prompt rather than from the product. Asked to document a feature it cannot see, a model will produce the most statistically typical version of that feature. It will be plausible, conventional, and about somebody else's software.

Skipping review because the output looks finished. Polished formatting reads as completed work in a way a rough human draft does not. This is the single largest risk in the whole practice.

Confusing volume with coverage. Two hundred generated articles nobody asked for is not better documentation than fifteen written against real questions from your search logs and ticket queue.

Assuming generation removes the need for an owner. Somebody still has to decide what should exist, what is wrong, and what to delete. That was always the expensive part.

When this stops scaling

The inversion is worth stating plainly: generation scales without limit and review does not. One reviewer who knows the product can genuinely verify a handful of articles a day, less if the subject matter is technical, and that number does not improve with better drafts. So the ceiling on how much documentation you can responsibly publish is a review ceiling, and it sits wherever your team's attention runs out - typically far below what the generation step can produce in an afternoon.

Two things follow. Generate against a source the model can read - a codebase, a diff, a recorded flow - so review is checking work rather than fact-finding from scratch. And treat review capacity as the constraint you plan around, publishing to it rather than past it. SupportPages is one of the tools in this category, which is worth knowing while reading this paragraph: it drafts from the codebase and puts articles in front of a person to approve. The condition under which any tool here is the right call is the same, and it is not about the tool - it is whether somebody is going to read the output before your customers do.