This paper presents a technical review of OpenAI’s large language models (LLMs), focusing on their transformer-based architecture, training methods, and real-world applications. It also highlights key engineering challenges such as bias, interpretability, and computational efficiency, while suggesting future research directions in alignment and model optimisation.