Though Large Language Models (LLMs) have been serving global users through a wide range of services, concerns remain regarding their cultural bias and misalignment with people of underrepresented communities. Increasing use of LLMs presents significant implications, as they have the potential to influence people’s original values toward a certain cultural perspective. Cultural alignment of LLMs with culture-specific knowledge offers a suitable solution to this concern. In our participation in the Semeval-2026 Task 7 we considered a prompt engineering-based cultural alignment strategy to address the cultural knowledge gap in LLMs. Our approach achieved promising 86.34% accuracy for Japanese culture-relevant multiple-choice questions from the BLEND benchmark.