Why a Chatbot Remembers a Preference but Still Gets It Wrong

Effective chatbot personalization requires distinguishing explicit, inferred, and confirmed preferences while accounting for whether information is current, authoritative, and applicable to a particular task or project. Temporary requests should be stored as scoped exceptions rather than overwriting enduring preferences, and high-impact decisions should rely on current application records instead of memory alone. Testing should define clear precedence among general, project-specific, and current-request preferences, while logging the records and versions delivered to the answer system helps separate retrieval failures from application failures. Evaluation should focus on real tasks rather than simple memory recall, verify corrections across new sessions, users, and projects, and ensure deleted records do not reappear after synchronization. Failures should be categorized by their cause and severity, with end-to-end testing beginning from a manageable preference that users can view, edit, and remove.

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