This paper explores the use of large language models (LLMs) as text classifiers for building smart expert systems. It investigates the performance of LLMs, such as GPT-4, in text classification tasks across various domains. The paper also examines the potential of LLMs for few-shot learning and fine-tuning in these classification tasks.