How One Developer Tamed LLM Hallucinations Inside a Drag-and-Drop Editor

A developer building WebDigitize, an AI-powered website generator for Nigerian small businesses, documented the structural challenges of integrating an LLM into the Puck drag-and-drop editor. Unlike typical AI use cases, the editor required model outputs to match exact JSON prop keys, since a single hallucinated field name silently breaks a component without any error or warning. Prose-based component descriptions proved unreliable at scale, so the developer switched to a compact formal schema notation listing strict prop keys, types, and inline constraints. Placing these schemas in the system prompt rather than the user message was critical, as schemas in user messages were occasionally overridden by the model during multi-turn critic-and-refinement passes. The writeup concludes that structural validity, not just semantic quality, must be a first-class concern when embedding LLMs into schema-driven editors.