Annotation guidelines are a set of rules and instructions that help individuals annotate data consistently and accurately. These guidelines outline how to label or categorize information, ensuring that everyone involved in the annotation process understands the criteria and standards to follow. This is crucial for maintaining quality and reliability in datasets used for tasks like machine learning or research. Following annotation guidelines helps reduce errors and improves the overall quality of the annotated data. Clear examples and definitions are often included to illustrate how to handle specific cases, making it easier for annotators to apply the rules effectively. This structured approach is essential for projects involving natural language processing or computer vision.