[quantadev]

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.

[Kiro]

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.

[quantadev]

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.