This survey article explores the development and applications of OpenAI's Large Language Models (LLMs), with a focus on ChatGPT. Recent advancements in natural language processing (NLP) have led to the creation of models capable of generating coherent and contextually relevant responses, with LLMs like GPT playing a significant role. These models are trained on vast datasets, allowing them to understand and generate text that is contextually appropriate and semantically rich. However, they also face challenges such as ethical dilemmas and the potential for spreading misinformation. ChatGPT, a variant of GPT based on transformer architectures, uses self-attention mechanisms and reinforcement learning-based human feedback to generate contextually appropriate outputs. Despite its advancements, there is a lack of comprehensive discussion on its architecture, effectiveness, and limitations. This survey aims to provide an in-depth analysis of ChatGPT's structure and performance, highlighting its ability to produce text indistinguishable from human writing, while also acknowledging its limitations and susceptibility to bias. The article also discusses the ethical and societal implications of this technology and the future of NLP and AI. The study provides insights into the inner workings of ChatGPT and the potential of LLMs in shaping the future of technology and society. The Eco-GPT approach, which uses a three-level cascade (GPT-J, J1-G, GPT-4), achieves significant cost savings in specific datasets. The article also highlights the rapid growth of ChatGPT's user base and its impact across various industries, demonstrating its widespread adoption and influence. The survey underscores the importance of enhancing the performance of GPT LLMs to meet the growing demand and address the challenges associated with their deployment.This survey article explores the development and applications of OpenAI's Large Language Models (LLMs), with a focus on ChatGPT. Recent advancements in natural language processing (NLP) have led to the creation of models capable of generating coherent and contextually relevant responses, with LLMs like GPT playing a significant role. These models are trained on vast datasets, allowing them to understand and generate text that is contextually appropriate and semantically rich. However, they also face challenges such as ethical dilemmas and the potential for spreading misinformation. ChatGPT, a variant of GPT based on transformer architectures, uses self-attention mechanisms and reinforcement learning-based human feedback to generate contextually appropriate outputs. Despite its advancements, there is a lack of comprehensive discussion on its architecture, effectiveness, and limitations. This survey aims to provide an in-depth analysis of ChatGPT's structure and performance, highlighting its ability to produce text indistinguishable from human writing, while also acknowledging its limitations and susceptibility to bias. The article also discusses the ethical and societal implications of this technology and the future of NLP and AI. The study provides insights into the inner workings of ChatGPT and the potential of LLMs in shaping the future of technology and society. The Eco-GPT approach, which uses a three-level cascade (GPT-J, J1-G, GPT-4), achieves significant cost savings in specific datasets. The article also highlights the rapid growth of ChatGPT's user base and its impact across various industries, demonstrating its widespread adoption and influence. The survey underscores the importance of enhancing the performance of GPT LLMs to meet the growing demand and address the challenges associated with their deployment.