Text annotation in Artificial Intelligence (AI) is the process of labeling or annotating text data to make it understandable for machine learning models. This process involves identifying and labeling specific components or features in text data, such as entities, sentiments, or relationships, to train AI models effectively. Text annotation can be categorized into several types, including named entity recognition, sentiment analysis, text classification, part-of-speech tagging, coreference resolution, dependency parsing, semantic role labeling, temporal annotation, and intent annotation. The quality of annotated data directly impacts the effectiveness of machine learning models, and establishing precise rules and frameworks for annotation ensures consistency across annotators. Advanced text annotation techniques include zero-shot and few-shot learning with large language models, prompt engineering, integration with annotation platforms, and generative AI models that streamline the annotation workflow and reduce manual effort. Text annotation can be used in various domains, such as healthcare, e-commerce, and sentiment analysis, to enhance applications, improve data understanding, and provide better end-user experiences.