AI documentation generator

What it reads, and what it writes

The input is whatever describes the product: source files, configuration, comments, READMEs, existing pages. The output ranges widely between tools — from docstrings on individual functions, to an API reference, to complete user guides organized around what a reader is trying to do.

That range matters when comparing tools. A generator that annotates functions and one that writes a getting-started guide are solving different problems, and both get called by the same name.

Generation is the easy half

Language models write fluent, confident prose about almost anything. That is exactly the risk: a generated page reads the same whether or not it is correct. A plausible parameter name, a reasonable-sounding default, an example that looks right — none of it is evidence.

So the meaningful question about a generator is not how good its writing is. It is how you know the output is true, and how you find out when it stops being true.

What to look for

• Grounded in the code. Is the output derived from the repository, or from a prompt and the model's general knowledge? Only the first can be checked against something.
• Stays current. What happens on the next release? A generator run once produces a snapshot that starts drifting immediately.
• Measurable. Can you see accuracy and coverage, or only the pages?
• Respects edits. When a person corrects a page, does the correction survive the next run?
• Readable by agents. Is the result published in forms AI assistants can consume, or only as HTML?

What it cannot supply on its own

Code says what a system does. It rarely says why a design was chosen, which path a new user should take, or what a team has decided not to support. A generator can draft around those gaps; the knowledge itself has to come from the people who own the product.