There are usually two of them and they behave differently. An input filter reads the prompt and refuses before any compute is spent, which is why a rejection can be instant. An output filter inspects the finished frames and suppresses them, which is why the other kind of rejection arrives after the wait, sometimes as a blank or black result rather than an error.
Filters are classifiers, so they are wrong in both directions. False positives are the common annoyance — anatomical terms in a medical brief, violence in a sports context, a brand name that collides with something else. False negatives exist too, which is why a provider's terms, not the filter, define what you are allowed to publish.
Where a tolerance setting is exposed, it moves a threshold within limits the provider set; it is not an off switch, and the provider's policy still governs regardless of the value.
In practice
- Instant refusals are usually prompt-level; late blanks are usually output-level.
- Rephrasing around a trigger word often clears a false positive without changing the brief.
- Filters differ per provider, so the same prompt can pass on one model and fail on another.
The mistake to avoid
Reading a blocked generation as a bug and retrying identically. Retries of a prompt-level refusal fail the same way; the wording has to change.
Related terms
Negative prompt
Negative prompt meaning: a list of what you do not want in the output, steering generation away from those terms.
Prompt
Prompt meaning: the written instruction a generative model reads to decide what to make. The one input almost every model needs.
Watermark
Watermark meaning: a mark on generated media that identifies the source, either visible on the picture or embedded in the file.
Prompt expansion
Prompt expansion is a provider rewrite of your short prompt into a longer one before generation, adding lens, light, texture, and mood you did not write.
Seed
Seed meaning: the number that sets the random starting noise so the same seed and settings can reproduce the same output.
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