Example Concept: Before training, an AI model needs labeled data if it's for supervised learning, which includes input-output pairs that the model can learn from. For unsupervised learning, the data doesn't need labels but should be rich in features that the model can analyze to find patterns. Additionally, the data should be clean, diverse, and representative of the real-world scenarios the model will encounter.