Here is a short example that came up for me last week.

I had a set of documents I wanted to classify according a taxonomy that is well known (so it is exists in the training data of all the major llm models I tested)

If I have prompt like, `You are an expert classification system. Using the Classification Approach Foo, consider the following and output the category in JSON format, such as {"class":"bar"} `

This works ok, but it works much better if I tell it to output {"class":"bar", "reason": "baz"} and improved with some other approaches like adding "related_class" or "parent_category" which would otherwise be redundant.

Also including some few-shot examples helped, but the biggest benefit came from the "reason" field. Trying justification or other synonyms seems to produce the same output.

I suspect this is something similar to CoT.

Speaking only for myself these ideas are a combination of things I've seen scanning new papers and informal discussions with other people working in the area. Feel free to shoot me an e-mail though, maybe I can point you somewhere more specific.

Edit: The "verbosity sink" name is inspired by the idea from the paper below although they're not actually at all the same thing.

https://arxiv.org/abs/2309.17453