Contextual Guides

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Comprehensive: The data should cover a wide range of examples and scenarios to ensure the AI can generalize well.
High-quality: The data should be accurate and free of errors, and should be labeled correctly to ensure the AI can learn effectively.
Relevant: The data should be closely related to the task or problem the AI is being trained to solve, and should contain all the information needed to perform that task.
Structured: The data should be organized in a way that makes it easy for the AI to access and use the information it contains.
Diverse: The data should include examples from different groups, cultures, and backgrounds, to ensure the AI can make fair and unbiased decisions.
In context: The data should contain as much contextual information as possible, such as the time, location, and circumstances under which it was collected, to ensure the AI can make use of that information.
Balanced: The data should be balanced in terms of positive, negative and neutral examples, to ensure the model does not overfit to one category
Tested: The data should be tested on a representative sample of the population to which the model will be deployed, to ensure the model generalizes well.
Continuously updated: The data should be continuously updated to reflect the changing environment and to improve the model performance.