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You have 200,000 support tickets and no taxonomy. Reading a sample gives you the loud categories and misses the long tail, which is where the interesting failures live. Clustering embeddings is the standard answer, and the standard implementation of it has two steps most write-ups skip.
Routing a support ticket into one of forty categories does not obviously need a language model. It needs a function from text to a label, and if you have a few thousand labelled examples there is a much cheaper function available: embed the text once, then run a linear classifier over the vector...
“200k context” is a statement about what the serving stack will accept, not a promise about what the model can use. The two numbers are related but they are not the same, and the gap between them has been measured — by people who published their method. What the advertised number is For an open...