Abstract
The article examines the transition from dialog assistants to the generation of interactive user interfaces powered by React libraries and large language models, driven by increasing expectations for turnkey operation and by the limitations of linear chat for complex, multi-step tasks. The purpose of the study is to conceptualize and compare engineering trajectories for generating React interfaces from LLM output, and to formulate architectural principles that ensure behavioral predictability, controllable interactivity, and robust user experience. The relevance of the research is determined by the rapid adoption of generative systems in analytical dashboards, administrative interfaces, and visual analytics scenarios, where iterative query refinement is required without loss of context and with preserved control over data and security. The novelty of the work lies in the systematic comparison of the approach of directly generating component source code with that of generating a structured specification, followed by validation and rendering a tree of pre-selected React components. It is shown that the specification track, in combination with caching, layout contracts, partial updates, and mature testing and observability practices, enables reducing the gap between formal correctness and functional suitability of generated interfaces and localizing risks at the boundary between natural language and executable behavior. The article is intended for researchers of dialog and interface systems, frontend architecture engineers, design system developers, and practitioners implementing generative UI pipelines in products with high interactive load.