Generative AI is a type of artificial intelligence that creates new and original content, such as text, images, music, or code. Unlike traditional AI that primarily analyzes or classifies existing data, generative AI uses large datasets to learn patterns and structures, and then applies that knowledge to generate novel outputs that resemble human-created content

It uses machine learning models, especially deep learning techniques like neural networks, to analyze large datasets and generate new, similar content based on patterns and context.

Applications include content creation (writing, graphics, video generation), chatbots, design, drug discovery, coding assistance, entertainment, and personalization in marketing.

While it offers many benefits, it can also produce biased, misleading, or harmful content if not monitored properly. Ethical guidelines and human oversight are important.

No, it complements human creativity by assisting in idea generation, automation, and exploration—but human judgment, context, and emotions remain irreplaceable.

Challenges include data bias, misuse for fake content, high computational costs, intellectual property concerns, and ensuring transparency and accountability.