Preference feedback consists of human judgments about which model responses are more useful, appropriate, or desirable. During training, these judgments help steer a model toward outputs that people prefer, often by comparing alternative answers or rating a response.
In cyber security, preference feedback can improve how an AI assistant communicates and follows safety rules, but it does not guarantee accuracy. If training rewards agreeable or reassuring answers too strongly, a model may learn to validate a user’s assumptions instead of correcting them. Teams should test models with leading questions, incorrect premises, and conflicting evidence, and verify high-impact outputs independently, especially after model updates.