Text Classifiers are an Underrated Application of LLMs

Before LLMs really became a thing, getting up and running with a text classifier for a non-standard problem from scratch, including the annotation of a dataset for training, would probably take at least 3 weeks of work hours. That amounts to 7,200 minutes. Today, getting up and running with a classifier using LLMs requires only writing a prompt, which takes about a minute.

That's a 7,200x productivity gain in the initial process of working with text classifiers.

One thing to note, however, is that in the 1-minute prompt scenario, you have collected zero data and therefore have nothing to measure your classifier's performance against. However, since you have a classifier, you can annotate much more efficiently using an active learning approach, and you have 7,199 minutes to knock yourself out with evaluating your classifier.

Everybody talks about chatbots and agents as the hot new thing, but honestly, a 7,200x productivity gain in text classifier development is also pretty huge!