Monitoring LLMs fundamentals
As LLMs become embedded in critical workflows and decision-making processes across various sectors, it is necessary to ensure these models perform consistently and reliably. Monitoring provides insights into a model’s performance to inform decisions on necessary enhancements, scalability, and decommissioning.
Maintaining consistent performance
Continuous monitoring is one component of managing LLMs, particularly because these models are prone to variations in performance due to changes in input data. This regular oversight helps ensure that the LLM delivers outputs that are accurate and consistent over time, which is vital in applications where unexpected deviations could have significant repercussions.
For instance, in financial services, an LLM that assists with credit scoring must produce stable and reliable evaluations. Sudden, unexplained shifts in how scores are calculated due to input data changes could lead to incorrect credit decisions...
