Comment by lambda

LLMs are originally trained to predict the next word in (mostly) human authored text.

Then they are fine tuned to follow instructions, and further reinforcement learning applied to make them behave in certain ways, be better at math and coding, etc.

They don't have any intrinsic motivation of their own, but they can try to parrot what they've seen in their training data.

So sometimes how you interact with them can affect how they interact, because they are following patterns they've seen in their source text.

However, a lot of folks use this to cargo cult particular prompting techniques, that might have seemed to work once but it can be hard to show that statistically they work better. Sometimes perturbing your prompt can help, sometimes you just needed to try again because you randomly hit the right path through the latent space.

I think your approach is probably a better one, for the most part trying to vary your prompt style is most likely to just affect the style of the output, so if you prefer a dry technical style, prompting it with one is the best way to get that out as well.