The most challenging task for machine learning teams is extracting the true value of ‘gen AI’ as it requires training on enterprise-specific proprietary data. Referred to as the ‘data bottleneck’, the problem addresses the inability of organizations to rapidly extract value from LLMs due to challenges pertaining to training data availability, quality, or sensitivity.
Download this solution brief to learn how synthetic data can be used to:
- Augment sensitive data safe, making it safe to expose to a public LLM.
- Augment limited datasets to ensure sufficient volume for model training.
- Boost underrepresented classes to remove unwanted bias in downstream tasks.
- Simulate new scenarios to ensure generalizability and performance of generative AI applications.
