Best practices:
- Validate synthetic data: Rigorously test its quality and representativeness.
- Use hybrid approaches: Combine synthetic and real data in training.
- Monitor performance: Regularly assess models trained on synthetic data against real-world benchmarks.
- Iterative refinement: Continuously improve synthetic data generation techniques.
In conclusion, while synthetic data can be highly valuable for AI training and knowledge expansion, its effective use requires careful management and ongoing validation to prevent degradation and ensure it remains representative of the real-world phenomena it's meant to model.