Diffusion Language Models (DLMs) have emerged as a promising alternative to Autoregressive Language Models, yet their inference strategies largely rely on prefix-based prompting inherited from the autoregressive paradigm. In this paper, we propose Template Infilling (TI), a conditioning methodology tailored for DLMs. Unlike conventional prefix prompting, TI distributes structural anchors across the target response, establishing a global template before infilling masked segments. This enables structured conditioning that leverages the bidirectional generation process of DLMs. We evaluate TI on diverse benchmarks, including mathematical reasoning, code generation, and trip planning, achieving consistent improvements of 9.40%p over baseline prompting strategies. Furthermore, TI naturally supports multi-token generation settings, providing practical speed advantages while maintaining generation quality and robustness. Overall, our results highlight a DLM-specific conditioning paradigm for structured generation, suggesting a promising direction for inference methods tailored to diffusion-based language models.