The more info you give the AI the more likely it is to utilize the practices it was trained on as applied to _your_ situation, as opposed to random stereotypical situations that don't apply.
LLMs are like humans in this regard. You never get a human to follow instructions better by omitting parts of the instructions. Even if you're just wanting the LLM to be creative and explore random ideas, you're _still_ better off to _tell_ it that. lol.
Not true and the trick for you to get better results is to let go of this incorrect assumption you have. If a human is an expert in JavaScript and you tell them to use Rust for a task that can be done in JavaScript, the results will be worse than if you just let them use what they know.
The only way that analogy remotely maps onto reality in the world of LLMs would be in a `Mixture of Experts` system where small LLMs have been trained on a specific area like math or chemistry, and a sort of 'Router pre-Inference' is done to select which model to send to, so that if there was a bug in a MoE system and it routed to the wrong 'Expert' then quality would reduce.
However _even_ in a MoE system you _still_ always get better outputs when your prompting is clear with as much relevant detail as you have. They never do better because of being unconstrained as you mistakenly believe.
LLMs are like humans in this regard. You never get a human to follow instructions better by omitting parts of the instructions. Even if you're just wanting the LLM to be creative and explore random ideas, you're _still_ better off to _tell_ it that. lol.