Weekly is the right default cadence for checking how ChatGPT, Claude and Perplexity answer your buying questions — monthly is the floor, and it's too slow to catch most drift while it's still cheap to fix. A single AI visibility check tells you where you stood the moment you asked; it says nothing about whether that's still true next week, because the answer to the exact same question can already look different by then. Here's why a one-off check goes stale fast, and how to build a cadence that actually tracks a trend instead of guessing from a snapshot.
One AI visibility check captures a single moment across a small number of questions and engines — it can't tell you whether that moment is representative or already out of date. That's true of any measurement of a probabilistic system: the same question, asked again, can come back with a different answer even with nothing about your brand having changed. Research on repeated-prompt trials finds that answer-consistency rates for language models vary widely by model and are routinely well short of 100%, even with randomness settings turned down.
Proof: arXiv — The Non-Determinism of LLMs: Evidence of Low Answer Consistency in Repetition Trials.
Bottom line: if the channel itself is non-deterministic, a single reading isn't a measurement — it's one sample from a distribution you haven't seen the rest of yet.
Three things can change your AI visibility without you doing anything: the model itself, the sources it's drawing on, and your competitors' content. AI labs update and fine-tune their models on their own schedule, which can shift which sources an engine treats as authoritative. Engines with live web search re-crawl pages continuously, so a competitor's new comparison page or a fresh review can enter the answer pool within days. None of this shows up in a one-time check — only a repeated one catches it while it's still new.
Why it matters: the things that change your visibility are largely outside your control and invisible until you look again — which is the whole argument for looking on a schedule instead of once.
Weekly checks catch a slide while it's still one or two bad answers, not an entrenched pattern; monthly checks let the same problem run for weeks before anyone notices. This is exactly the same measurement discipline behind AI brand intelligence more broadly — recognition, description, share of voice, sources and accuracy are only useful tracked over time, not read once and filed away. A monthly cadence isn't wrong, but treat it as the minimum viable frequency, not the target.
Do this: if you can only afford one cadence, make it weekly for your core buying questions and monthly for the long tail.
Certain moments deserve an extra check outside your regular schedule: right after you ship a fix, right after a competitor's launch or funding news, and right after any of the big AI labs ships a model update. Each of those is a plausible cause of a real shift, and checking close to the event is the only way to tell whether it actually moved your numbers or whether the change was noise from run-to-run variability.
Rule of thumb: treat a launch, a competitor move, or a model update as a trigger for an unscheduled check — don't wait for your next regular run.
A re-check only tells you something if it repeats the same fixed set of buying questions, across the same engines, run more than once — otherwise you can't tell a real change from a different sample. Comparing this week's answer to five new questions against last month's answer to five different questions isn't tracking, it's noise. The same discipline that makes a share-of-voice number trustworthy applies here: fix the question set, fix the engines, and only then compare across time.
Shortcut: Reflexa's Weekly tracking re-runs your fixed question set on a schedule and shows the movement run-over-run, so you're comparing like with like automatically.
There's no cadence that guarantees you'll never be surprised — even weekly tracking samples a moving, probabilistic target, and a real shift can still happen between two checks. What a regular cadence buys you isn't certainty, it's a shorter delay between a problem appearing and you finding out about it, which is the only lever you actually control.
Sources: arXiv — Non-determinism of LLMs, repetition-trial consistency · What is Brand Intelligence? · Share of voice in AI answers
The free check runs Recognition across ChatGPT, Claude and Perplexity — 3 minutes, evidence included.