Modeling a queue system for generation of seo-optimized texts in a highly loaded environment

Abstract

The work is dedicated to the development of a system for automated generation of SEO-optimized texts with subsequent modeling of the queue system to reduce the costs of creating high-quality content, simplify the process of generating texts and ensure their compliance with modern SEO requirements. Research on mechanisms of interaction with AI services to optimize the process of generating texts under conditions of restrictions on the number of re-quests and data processing speed. Studies of existing solutions for automatic content genera-tion were conducted, including the consideration of services based on artificial intelligence, such as ChatGPT. As a result, limitations of existing approaches were identified, mainly re-lated to their insufficient scalability or API limitations. A Q-scheme was synthesized to solve a specific problem, an asynchronous modeling algorithm was adapted, and a modeling of the queue system of SEO-optimized texts was carried out. Improved algorithms for interacting with AI services ensure stable and efficient operation, allowing not only to reduce the cost of the content filling process, but also to make it as efficient as possible, while maintaining the quality of the texts at a level that is not inferior to the work of professional copywriters.