Text generation using GAN networks is becoming more effective but still requires new approaches to achieve stable training and controlled output. Applying feedback score to control text generation is one of the most important topics in NLP nowadays. Feedback or response is a natural part of conversations and not only consists of words, but also can take other shapes such as emotions, or other reactions. In dialogue processes feedback is a factor influencing the next phrase or reaction. Depending on this feedback or response we correct our possible answers by trying to change the tone, context, or even structure of the sentences. Applying feedback as part of the GAN model structure will give us new ways to apply feedback and generate well-controlled outputs with defined scores which is very important in realworld applications and systems. With GAN networks and their instability in training and unique architecture, it becomes trickier and requires new ways of solving this problem. The matter of feedback usages for text generation task using GAN networks we will review in this paper and experiment with integrating score values into GAN's generator model layers.