training.

But real-world human-labeled data is expensive and requires a lot of data science labor – and synthetic data is so low-quality and unrelated to the model’s actual use-case that it doesn’t improve performance much.

Nurdle can help. By using real-world human-labeled kernel datasets built for specific use-cases, we produce enhanced synthetic datasets that perform almost as well as human-labeled at the speed and price of synthetic datasets. Contact us for a free data gap analysis showing you what kind of data you’re missing and how much you need for your performance target.

Nurdle AI uses real-world custom use case datasets to generate synthetic datasets for accurate LLMs.