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.
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.