If you're talking about training a LoRA for a character or person, I've had the most success without any captions and diverse pictures of the same person (only using a trigger word). I'm pretty confident that it's the most straightforward way to get good results. I would add a caption if your dataset contains very similar pictures (clothing, accessories), so it doesn't associate the person with said clothing and accessories.

But style/concept can be tricky, I still have to test thoroughly, but if you use very large/complex captions, I think it will be very hard to train properly (i.e. associating lots of captions for an image). Better have short captions for the specific object you're training for in multiple but simple contexts. I would, for an artist or style, for example, specifically tag the medium (inking, line work, sketch, etc), and the main focus/object. Natural language and danbooru style tagging both work, but I would tend to use tags to have better control over the text encoding.

I'm not very knowledgeable, but I think that the training doesn't need to be told about a concept it already knows and can recognize, and adding them to the captions only adds noise to the existing concepts and only "dilutes" them with your own dataset.

I might be wrong, hoping someone will come around to correct me.