How often do AI answers change? Setting a monitoring cadence
The right monitoring cadence is the slowest one that still catches the changes you care about. Too fast burns credits on noise; too slow misses the week you disappeared from ChatGPT.
What actually changes an answer
- Model updates — infrequent but step-changes; an engine can rewrite its answer style overnight.
- Source drift — the continuous one. As the web changes, retrieved sources change, and the answer follows. This is daily-to-weekly movement.
- Prompt sensitivity — the same intent asked differently can produce different answers. Fix your prompt text; monitor phrasing separately if it matters.
- Personalization and geo — same prompt, different market, different answer. That’s why every run carries
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Cadence by use case
- Brand monitoring: daily for your top 20 prompts is enough to catch a disappearance within 24h; weekly for the long tail.
- Category/competitor tracking: weekly. Source drift matters more than daily noise.
- Launches and campaigns: daily during the window, then back to weekly.
- News and fast topics: hourly-to-daily, on Google News and AI answers to news queries.
- Baseline building: whatever cadence, run it for 4+ weeks before drawing conclusions — one run is an anecdote.
The credit math
A daily check of 100 prompts across 3 engines and 5 markets is 1,500 runs/day — ~45k/month, which sits inside the Lite plan for most engines. The lever isn’t cadence, it’s multiplication: trim markets and engines before you trim frequency. Pricing lists per-engine credit costs.
Don’t alert on noise
Answers vary run to run even with no real change. Alert on sustained shifts — brand missing 3 runs in a row, citation share down two weeks running — not single-run drops. AI share of voice has the metric definitions.