2. Hybrid Approaches

• Combine synthetic data with fresh, real-world data to maintain diversity and accuracy.
• Use synthetic data to augment rather than replace traditional datasets entirely.

3. Continuous Monitoring

• Implement systems to track model performance and detect signs of quality degradation or collapse.
• Regularly assess the diversity and relevance of model outputs when trained on synthetic data.