Applications And Challenges Of Large Language Models In Real-World Systems

Large Language Models (LLMs) have emerged as a major breakthrough in artificial intelligence, significantly improving how machines process and generate human language. These models, built on transformer architectures, are capable of performing a wide range of tasks such as text summarization, translation, question answering, and code generation. In this paper, we analyze the applications and limitations of LLMs in real-world systems through a qualitative study based on literature review and conceptual experimentation. Our findings suggest that while LLMs provide high accuracy and flexibility across domains like healthcare, education, and customer service, they still face critical challenges such as hallucination, bias, high computational cost, and lack of interpretability. The study highlights the importance of integrating validation mechanisms and ethical AI practices to ensure reliable deployment. We conclude that although LLMs are powerful tools, their practical adoption requires careful optimization and responsible usage strategies.