"If you know what those types of article are you can separate them out and make sure they're represented in the prompt. But now we're back at needing a tonne of domain knowledge about your problem -- that's like having to write custom features to make your model work"

I think this is where the different perspectives come into play.

If you're an NLP practitioner you are thinking, oh no! I need to know a lot about audience intention and how the articles are represented navigationally and the kind of variety people are looking for and how articles might fit multiple categories, etc, etc. And you have to think about these things on a meta level ("prompt engineering"), because you have to instruct the model on how to act in an abstract way.

If you're someone who wants to run a news site then you already are thinking a ton about these things, and probably have a dozen things you'd like to change and adjust, new ways of presenting content, etc. You _wish_ you could be thinking about these domain-specific topics.

What feels like a bug to the NLP practitioner – needing a deep understanding of the domain – is a feature to... everyone else. It's a feature to the people who care most about the results.

The other big perspective difference here, I believe, is how you think about goals. How many tasks ARE categorization? My intuition is that it's a quite small number. There are many tasks that can be implemented with one step as categorization, but that is seldom the task. To the NLP practitioner categorization might seem very prominent – that's when someone calls you up or hands over the work. But with an LLM you might be able to do a much larger portion of the real task, with or without categorization.

Even with a categorization task, when I'm working with an LLM I usually don't produce just a "category", but produce other information at the same time, often using natural language as a first-class data type because it can be fed back into an LLM. In my experience the results are often (usually?) much higher quality because I'm not breaking things down into steps where the inaccuracies propagate between steps, but instead going right for the result, and using a model that can basically "check" itself against general knowledge, scrubbing out nonsensical results during inference. (As a result the remaining inaccuracies often appear plausible and are labeled "hallucinations"... it can make things more challenging, but what we don't see are the multitude of obvious inaccuracies that a more traditional NLP system would create, and which in a sense exist momentarily during the LLM inference.)