No not at all, you got it backwards. This was originally called “chain of thought prompting”, and it basically explained a model on how to reason through a problem before providing an answer.

Because of the nature of how LLMs work — text prediction engines - by putting the explicit reasoning steps first, it improves the likelihood of the final answer (which then is being predicted based on the entire reasoning chain as input) being correct.