International Journal of Scientific Research and Engineering Development

Large Language Models (LLMs) emerge today powering, amongst other things, chatbots, code generation, knowledge systems, and creative tools. This review traces their foundations, evolution, training, alignment, and use. It contrasts the main families of models-GPT, Gemini, LLaMA, and Claude-tracing recent trends of scaling and open-weight distribution. In this backdrop, go-to key challenges stand to be trade-offs in efficiency, environmental cost, hallucinations, and biases. The future unfolds into multimodality, retrieval-augmented generation, explainability, and policy framework.