To tackle the dual challenges of incomplete requirements and hallucinations in large language models (LLMs), this paper proposes a business-logic-driven iterative requirements auto-completion approach named ReqCompleter. By treating the use case – entity– operation” triplet as the smallest computable closed loop, ReqCompleter adopts a model-driven iterative mechanism. First, a use-case model, an E-R diagram, and a CRUD (Create, Read, Update, Delete) matrix are fused into a unified semantic framework. Next, gaps in the CRUD matrix act as triggers to iteratively detect missing functionalities, while the E-R diagram delimits entity boundaries to steer the LLM toward generating requirements within a controlled scope. We evaluate our approach across seven cases in e-commerce, logistics, public safety and other domains. Compared to general-purpose LLMs, it improves requirements completeness rate by 20%-88% while reducing hallucination rate by 2.4%-13.9%. To the best of our knowledge, this work represents the first tight coupling of classical requirements engineering models with generative AI, establishing an automated closed-loop system that deliverswhat’s missing, as needed" under explicit business logic constraints. This opens a new and practical technical pathway for high-quality, explainable, and continuously evolvable requirements engineering.