This article details a system for handling media support tickets using LLMs, emphasizing per-tenant scheduling and strict JSON schema validation for classification tags. It proposes a Node.js-based intake handler that normalizes tickets with tenant IDs and version information before enqueuing them. The scheduling policy can be global FIFO, round-robin, or weighted, depending on tenant isolation needs and capacity commitments. The LLM's output must adhere to a defined JSON schema for routing, with unknown categories rejected to ensure accurate classification. AI
IMPACT Provides a framework for integrating LLMs into customer support workflows with improved efficiency and tenant-specific resource management.
RANK_REASON Article describes a technical implementation for using LLMs in a specific application (media ticket classification), rather than a new model release or core research.
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