DeepSeek prompt volatility tracking
DeepSeek volatility is dominated by whether search fired, not by model temperature. We report the search rate next to the volatility score so the cause is legible.
How it renders
Derived across the sample set, with search_rate alongside.
Sampling
Volatility is what tells you the right n for every other field. We recommend using it to allocate credits: sample noisy prompts deeply, stable prompts lightly.
Gotcha
What the API returns
A single collection call against DeepSeek for the prompt volatility field, sampled n times and returned as one object per prompt.
{
"engine": "deepseek",
"field": "prompt_volatility",
"prompt": "best ai visibility tracking tool",
"country": "GB",
"language": "en",
"n": 30,
"volatility": {
"jaccard": 0.42,
"mention_variance": 0.11,
"recommended_n": 75,
"band": "moderate"
}
}Response fields
| Key | Type | Notes |
|---|---|---|
| jaccard | float | Mean pairwise citation-set overlap across samples. |
| mention_variance | float | Variance in brand presence across samples. |
| recommended_n | integer | Samples needed for a ±10 point interval on this prompt. |
| band | string | stable | moderate | volatile. |
DeepSeek prompt volatility FAQ
- Why does DeepSeek search inconsistently?
- Its search trigger is prompt-sensitive. Small wording changes flip it, which is itself useful to know.
- Can I force search mode?
- Not reliably from outside. We report what the product does rather than manufacturing a mode that users would not get.
Find out which DeepSeek prompts are stable
We will run a volatility pass across your China prompt set and flag the coin-flips.
Other DeepSeek data
Across every engine
Compare how prompt volatility behaves on every engine we track.
Prompt volatility across all engines →