No single safeguard is enough. Each catches a different failure, and each has blind spots of its own. Pick a real incident and watch the claim move through the pipeline:
Incident presets
Each layer is a real, cited technique (detailed below); which layer catches a given incident here is illustrative judgment, not a tested finding.
A caught claim routes to one of three places: an answer with citations, once the correction is supported; an abstention, if it's still unresolved; or human review, when the stakes or the ambiguity are too high to automate.
Checking, not rereading
The "verification" layer above means checking a drafted claim against a signal the draft itself never touched (retrieved evidence, a calculator, a separate model) rather than just asking the same model to reread its own answer.[43] The gap between those two is large. In one study, checking each entity in a generated list individually raised accuracy from about 17% to about 70%, and adding a verification pass to biography generation raised its FActScore from 55.9 to 71.4, a 28% gain.[43] Self-critique alone rarely closes that gap, since a model reconsidering its own answer can just as easily repeat or rationalize the mistake as fix it: verification is strongest when it introduces a genuinely independent signal.[44]
Knowing what you don't know
Uncertainty routing is what decides whether a claim ships as an answer, gets held back as an abstention, or goes to a human, based on a confidence score, whether that's the model's own stated confidence or the sampling agreement from "Ask It Five Times." R-tuning trains that judgment directly into the model rather than leaving it to a prompt.[45] Its blind spot is the same one verification has. Fluency isn't confidence. A wrong claim stated cleanly can carry the same apparent certainty as a correct one, so routing only helps when the underlying signal actually tracks correctness: training tends to reward sounding sure over being right.[46]