It has been developed a traditional model based on Artificial Intelligence for natural language programming pursuant to RNN and a big dataset. The model makes its greatest contribution by improving the accuracy in the translation of informal language. The methodology consists on studying the training model that appy each of them (Goole Translate, Deepl and Bing). To do this, we have designed a dataset which includes more than 200 registers of poetic verses and 230 popular sentences - with differences between folk sayings and proverbs. Our aim is training a model and validating the results obtained from machine translators.