In recent years， Large Language Models （LLMs） have demonstrated excellent language understanding and dialogue capabilities in fields such as natural language processing and computer vision. However， they can produce inference results that are inconsistent with the correct answers in professional fields. This situation brings significant challenges to the application of LLMs in precise and accurate decision-making tasks. To solve this problem， a rule-guided Post Prompt of Large Language Model （PP-LLM） generation method was proposed. In this method， by generating post prompts， the original problem was transformed into two sub-problems that are easier to solve， thereby achieving the purposes of introducing expert knowledge and reducing the difficulty of task learning. Specifically， the knowledge-guided specific rules were used to transform the output part of the supervised dataset into a combination of post prompts and the output portion. PP-LLM method does not change the training and inference processes of the model， and does not add computational cost. Experimental results show that PP-LLM method significantly improves the accuracy of inference results and narrows the gap between model predictions and actual answers. Compared with the results without using the proposed method， the F1 value and Recall-Oriented Understudy for Gisting Evaluation （ROUGE） of the PP-LLM method have significantly improved. It can be seen that the above work improves the reliability of LLMs in professional applications and provides new ideas for LLM generation technology.