The need for large datasets is evident. Existing AI models like Gemma 7B require six trillion tokens for its training phase, and Llama 2 7B, require two trillion tokens. These two models are built for text generation and must be knowledgeable on a wide array of topics; therefore, the datasets can consist of general data such as publicly available web documents. Finding data to train models in niche domains is more difficult due to data scarcity. Even when real-life data is available, they can have issues like low data diversity, concerns about privacy regulations and a data imbalance.
AI engineers can address these issues by using synthetic data generation to train AI models in niche domains. Synthetic data will help models be more robust at handling a wider range of inputs, decrease time-to-market for AI applications, and improve model …