2 posts tagged with "ai-documentation"

Most product teams treat AI feature documentation as an afterthought — a help article that explains where to find the button and what happens when you click it. Then the support tickets start rolling in. "Why did the AI give me a different answer this time?" "How do I know if this result is accurate?" "It worked yesterday but not today." These aren't users being difficult. They're users whose mental model — built from your documentation — doesn't match how AI actually behaves.

Traditional how-to guides are designed for deterministic software. AI features are not deterministic. Closing that gap isn't a copywriting problem; it's a structural one. The documentation formats that work for a settings page will actively mislead users when applied to a language model.

Your documentation looked fine six months ago. It still looks fine today — individually. But a user filed a bug this week: two pages of your developer docs give opposite advice on the same configuration option. One page says to set max_retries to 3 for production workloads; another page says to leave it at the default of 0. Both were AI-generated. Both sound authoritative. One reflects what your system actually did in January; the other reflects how your AI tool interpreted a slightly different prompt in June. Nobody caught it because nobody was looking at the corpus as a whole.

This is AI content drift. It is not a hallucination problem. The AI was accurate at the time of generation. The drift happened in the gap between runs.