As LLMs increasingly output structured data, the challenge of rendering that data in user interfaces has spawned multiple paradigms. This article identifies five distinct approaches: hardcoded templates, configurable components, schema-driven rendering, semi-automated UI generation, and fully generative UI. Each paradigm balances flexibility, performance, and developer effort differently. For example, hardcoded templates offer simplicity but lack adaptability, while generative UI provides maximum flexibility at the cost of unpredictability and latency. The author provides real-world examples and decision criteria for choosing the right paradigm based on data complexity, update frequency, and user interaction needs. This taxonomy is valuable for teams building LLM-powered dashboards, report generators, or any application where AI-generated data must be visualized dynamically.
A structured taxonomy of five approaches to rendering LLM-extracted data in UIs, from hardcoded templates to generative UI, with practical trade-off analysis.