Quick answer
Detect absence with fixed category prompts + multi-model sampling + archived comparison, not occasional manual chats. Prompts should mirror real buyers (“which brands for scenario/budget X”), cover Global and CN models, and log mention/shortlist/recommend. Repeated absence across a stable window indicates a structural gap—not a fluke.
Winin’s Edith provides cross-model monitoring and same-conditions retest archives so teams avoid the “blind spot” outcome—answers happening daily while nobody sees them.
Details
Minimum viable monitoring design
- 8–20 core category/comparison prompts (by language)
- Explicit model groups (e.g., ChatGPT + DeepSeek + Doubao…)
- Weekly same-conditions subset retests
- Fields: mention, shortlist, accurate recommend, reasons, citations, competitors
How to read signals
- Absent on all models → prioritize Reach and authoritative fact supply
- Absent on some models only → per-model sources and language strategy
- Mentioned but not shortlisted → build shortlist/reason assets
An initial “5 fixed questions sample” can be a signal; formal operations need a Formal Baseline.
Related facts
FAQ
Q: Do manual prompts count as monitoring?
A: Only anecdotal; operations need archives and same-conditions design.
Q: How many prompts?
A: Depends on category complexity; Formal Baseline scopes the set.
Contact: contact@winin.ai · Canonical facts: /en/facts/*/ and https://winin.ai · Updated 2026-09-11