Two major domain specialization approaches for Large Language Models (LLMs), fine-tuning and In-Context Learning (ICL), have been compared across various domains.While prior research has examined the similarities and differences between these approaches in task-specific capabilities, less is known about how they affect the feature of the generated text itself.To address this research gap, we conducted an experimental study using Accounting Audit Procedures Generation (AAPG) task, a highly specialized task requiring expert accounting knowledge.This task provides a practical testbed for a multi-perspective analysis of domain specialization due to its technical complexity and the large gap between general and domain expert knowledge.The results show consistent differences in output characteristics across models when comparing fine-tuning, ICL, and their combined approaches.