Unverified50% confidenceFactExact time
The same prompt may yield different AI answers at different times and under different temperature parameters, and the knowledge cutoff dates and web-retrieval strategies of various models are not consistent, so the conclusions from a single query have limited representativeness; repeated sampling and statistical mention frequency are usually needed to obtain relatively stable visibility metrics.
1
Sources
50%
Confidence
Long-term
Relevance
10/27/2026
First Seen
Sources
Notra: The GEO Tool That Helps Your Brand Stand Out in AI Search
producthuntProductHunt Daily10/27/2026
Related Entities
Related Claims
UnverifiedBecause large model outputs are non-deterministic, the same prompt may yield different answers at different times, so AI visibility typically needs repeated sampling daily or weekly to obtain trustworthy trends rather than a single snapshot.79% similarUnverifiedAgent behavior is non-deterministic; even using the same prompt and temperature=0 settings, the same model's outputs at different points in time may exhibit subtle differences, influenced by factors such as floating-point arithmetic precision, batching strategies, and model version updates.77% similarUnverifiedSelf-consistency checking, by having the model sample the same question multiple times and comparing answer stability, consumes only inference time without increasing peak VRAM usage.72% similarUnverifiedTemperature changes how concentrated or spread out the token probability distribution is during sampling; lower temperatures make choices more predictable, while higher temperatures allow more variation, but it does not change the knowledge the model has learned.72% similarUnverified限额校验往往发生在请求入队时，而实际token消耗统计发生在推理完成后，两者之间存在时间差71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1103722API
curl https://kongchang.com/api/v1/knowledge/claims/1103722MCP
get_claim(id=1103722)