Large language models excel in tasks like summarization but can be too slow and costly for handling massive data volumes. Prioritizing accuracy in smaller, traditional models often yields better results for specific customer needs. The use of synthetic data is beneficial, but it must be balanced with real-world data to ensure effective model training.
