Although many teams first encounter LLMs as chat systems, they
are also powerful classification engines. They can assign labels directly through prompting,
produce rationales for audits, and adapt quickly to new taxonomies. That said, not every
classification workload should be handed to a general-purpose generator. Some workloads
still favor smaller discriminative models or hybrid pipelines.
A strong engineering answer compares approaches rather than treating LLM classification
as a universal upgrade. The right choice depends on class complexity, data volume,
explanation needs, latency targets, and whether the label space changes frequently.