Comparative Analysis of Generic and Specialized Natural Language Processing Models Using Prompt Engineering

Recent advances in Natural Language Processing (NLP) have been driven by the widespread adoption of large-scale pretrained language models (LMs). While generic NLP models such as GPT, BERT, and T5 exhibit strong zero-shot and few-shot performance across diverse tasks, specialized NLP models (e.g., BioBERT, FinBERT, SciBERT) are fine-tuned on domain-specific corpora to achieve superior performance in targeted applications. With the emergence of prompt engineering as a method to guide large language models (LLMs), a new research challenge arises: can prompt engineering narrow the performance gap between generic and specialized models, or does domain-specific pretraining remain necessary? This paper provides a comparative analysis of generic and specialized NLP models under different prompt-engineering strategies, focusing on domains such as finance, healthcare, and legal text processing. Experimental findings indicate that while prompt engineering enhances the adaptability of generic LMs, specialized models continue to outperform in precision-critical tasks. The study underscores the complementary role of prompt design and domain-specific adaptation in the next generation of NLP systems