Monitoring LLM Drift Over Time: A Longitudinal Example

LLM answers are not static.
As models update, their descriptions of companies can drift — even without changes in underlying facts.
This case shows how periodic monitoring of LLM outputs against a verified profile reveals silent drift: missing details, altered wording, or reintroduced inaccuracies.
Treating LLM output monitoring as a continuous process, rather than a one-time check, becomes essential for long-term reliability.

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