Let the model invent the tags, then match them to your real ones with embeddings

Doug Turnbull’s technique, written up by Simon Willison on 14 August 2026, inverts the usual classification prompt. Instead of pasting a controlled vocabulary into the context, which is impractical when the vocabulary is large (Willison notes his own blog carries 1,856 tags), you ask the model to invent tags freely, show it examples of the hierarchy you want, then use vector embeddings to match each invented tag to the nearest real one. It is presented as an approach rather than a measured result: no accuracy figures or failure cases are given.

What it means Anyone who has tried to classify against a taxonomy larger than the context window has hit this wall, and the trick turns the model’s tendency to invent into the useful half of the pipeline. It is an idea rather than a benchmark, so measure it on your own labels before shipping it.