Using neural network tools to accelerate the development of Web interfaces

Abstract. The article is devoted to considering modern neural network tools that allow for speeding up the development of web interfaces and simplifying the work of UI/UX designers. One of the main problems of modern design is quick access to general information and possible structuring of a site with specialized content, as well as obtaining its visual content. Currently, neural networks cannot replace designers, but to a large extent help them solve tasks. All neural networks that can be used in the design of web interfaces can be divided into four main types: convolutional, recurrent, forward propagation, and generative adversarial networks. In his work, the designer can mainly use generative networks, they can be classified according to the principle of "information at the input - information at the output". When working on a project, the designer can create a request to the neural network and get several options, generate different ideas, and create mood boards based on them, selecting colors, gradients, texture, typography, etc. The neural network can create various graphic elements: icons, buttons, illustrations, and photos with the right perspective, style, and colors. Using neural networks to improve images and refine or remove necessary elements is also promising. The process of speeding up the creation of the landing page interface using the Midjourney application is considered. Examples of writing prompts (prompts) that will affect the final quality of the generated image are given. The results are high-quality visual content that can either be placed in a project or used as an idea, element placement, composition, color scheme, photos, icons, etc. After creating the graphic design elements using Chat GPT 3.5, the landing page's content was created. You can use the FIG GPT plugin directly in the Figma environment to quickly generate the required content. Existing shortcomings and generation inaccuracies that arise in the work can be corrected by quickly updating and creating new versions of neural networks.