Don't classify. Hallucinate!

Managing a large collection of blog content often leaves older posts untagged. When a tagging system contains nearly two thousand categories, the list becomes too large to feed into a language model all at once to find direct matches.

A practical solution involves changing how the artificial intelligence is prompted. Instead of asking the model to select from an existing vocabulary, the system instructs the model to hallucinate or invent new classifications and tags that fit the content.

Providing a structural example within the prompt helps the model generate useful guesses. For a query about a brown coffee table, the model might imagine a relevant category path without knowing the database beforehand.

Once the model produces these imagined tags, the system applies vector embeddings to compare them against the existing corpus. This mathematical step identifies the concrete, pre-existing tags in the database that are closest to the ones the model newly generated.

This approach matters because it bypasses the token limit and confusion of feeding massive tag lists directly into a language model. It allows automated tagging systems to accurately categorize large archives by bridging creative generation with precise database matching.