This article describes a pattern where a small LLM generates plausible but fake category labels for a query instead of being constrained to a fixed taxonomy via structured outputs.

The hallucinated labels are resolved to real taxonomy entries by computing embedding similarity against pre-computed embeddings of valid classifications. The approach eliminates the need to ship large schema lists to the model and enables classification with cheaper models at scale.