With the rapid progress of digital transformation, the manual completion of administrative forms remains inefficient and error prone, creating significant challenges for public service delivery. Large Language Models (LLMs) offer strong potential for structured text generation, yet their adoption in this domain is hindered by concerns over privacy and reliability. To address these issues, we introduce the TagFill System, which automatically replaces blank fields in Vietnamese administrative forms with semantically meaningful tags (e.g., [user1_full_name]) rather than real values, thereby preserving user privacy. Our approach combines in-context learning with a post-processing pipeline designed to enhance reliability and reduce common LLM errors. We further construct both synthetic and real-world datasets to facilitate comprehensive evaluation. Experimental results with Gemini-2.0-Flash and GPT-4o-mini show that nearly 70% of fields can be correctly tagged, with over 95% accuracy on predefined fields. These findings demonstrate the feasibility of applying LLMs to privacy-preserving form automation and lay the groundwork for future integration with value mapping in public service workflows.