This paper presents a comprehensive comparative analysis of nine prominent generative artificial intelligence (AI) tools used in Natural Language Processing (NLP), including ChatGPT, Perplexity AI, YouChat, ChatSonic, Google’s Bard, Microsoft Bing Assistant, HuggingChat, Jasper AI, and Quora’s Poe. The study evaluates the pros and cons of each tool, highlighting their strengths and weaknesses. It also explores the transformative impact of generative AI in NLP, focusing on its integration with search engines, privacy concerns, and ethical implications. The paper categorizes the tools based on popularity and discusses the challenges in development, such as data limitations and computational costs. Additionally, it delves into AI planning techniques in NLP, covering classical planning, probabilistic planning, hierarchical planning, temporal planning, knowledge-driven planning, and neural planning models. The study concludes by providing an overview of the current state of generative AI, including its challenges, ethical considerations, and potential applications, contributing to the academic discourse on human-computer interaction.This paper presents a comprehensive comparative analysis of nine prominent generative artificial intelligence (AI) tools used in Natural Language Processing (NLP), including ChatGPT, Perplexity AI, YouChat, ChatSonic, Google’s Bard, Microsoft Bing Assistant, HuggingChat, Jasper AI, and Quora’s Poe. The study evaluates the pros and cons of each tool, highlighting their strengths and weaknesses. It also explores the transformative impact of generative AI in NLP, focusing on its integration with search engines, privacy concerns, and ethical implications. The paper categorizes the tools based on popularity and discusses the challenges in development, such as data limitations and computational costs. Additionally, it delves into AI planning techniques in NLP, covering classical planning, probabilistic planning, hierarchical planning, temporal planning, knowledge-driven planning, and neural planning models. The study concludes by providing an overview of the current state of generative AI, including its challenges, ethical considerations, and potential applications, contributing to the academic discourse on human-computer interaction.