We present our solution for the Russian RDF002 to-text generation task of the WebNLG Challenge 2023. We use the pretrained large language model named FRED-T5 (Zmitrovich et al., 2023) to finetune on the train dataset. Also, we propose several types of prompt and run experiments to analyze their effectiveness. Our submission achieves 0.373 TER on the test dataset, taking the first place according to the results of the automatic evaluation and outperforming the best result of the previous challenge by 0.025. The code of our solution is available at the following link: https://github.com/Ivan30003/webnlg_interno