Published July 29, 2026, this article introduces LettucePrevent, a system that integrates hallucination detection directly into the language model generation loop rather than checking outputs after the fact. The method uses a token-level detector that evaluates candidate tokens against source context and penalizes logits for unsupported content before sampling occurs. A regex-based number detector achieved reductions of 60% or greater in numeric hallucinations across models tested, while a neural factual detector showed meaningful results only on Llama-2-7B and came at the cost of substantially higher latency.
