[ytoawwhra92]

For many of these tools your prompt is a small part of the initial input, and it shrinks as a proportion of the total as more information is added to the context.

IME you can get great results from crappy prompts if the surrounding context is high quality, and you'll often get terrible results from great prompts if the surrounding context is low quality or non-existent. Same goes for the training corpus, I imagine.

I think success with LLMs is dominated by giving them a well-structured workspace, high-quality reference material, and strict tools with which they can check their work. Then you only need great prompts when venturing into parts unknown. But for that stuff you may as well write it by hand and get the LLM to fill in the gaps IMO.

[HarHarVeryFunny]

True - and some may have been RLHF’d to give more rigorous/expert responses regardless of how the question/request is posed. Still, others responding here say that swearing at the model can be helpful (a signal to cut the BS I suppose), and an LLM wouldn’t be doing the job it was trained for if it’s response wasn’t highly attuned to the input