Building privacy-preserving evaluation benchmarks with synthetic data offers a powerful solution for validating AI systems in regulated industries like healthcare, finance, and government. Traditional benchmarking faces major hurdles due to data privacy laws, limited access to real-world data, high annotation costs, and scarcity of edge cases. Synthetic data enables teams to overcome these barriers by generating realistic, labeled datasets that mimic real conditions without exposing sensitive information. The process begins with creating high-quality synthetic data using tools like NVIDIA NeMo Data Designer. In a healthcare example, the goal is to predict Emergency Severity Index (ESI) levels from emergency room triage notes—critical for prioritizing patient care. Real triage notes are protected by HIPAA and other regulations, making them unavailable for model development. Instead, synthetic data is generated by defining structured prompts and constraints that reflect authentic clinical language, patient demographics, and triage scenarios. Using NeMo Data Designer, developers first set up a client connection and configure LLMs for content generation and quality evaluation. They define data columns using samplers to generate attributes such as unique record IDs, ESI levels, clinical scenarios, patient profiles, and writing styles. A Jinja-templated prompt injects these values into the LLM to produce realistic triage notes in the telegraphic style used by nurses. To ensure quality, an LLM-based judge evaluates each note for clinical coherence and complexity, filtering out low-quality or inconsistent outputs. Once the synthetic dataset is generated—thousands of labeled examples in minutes—developers move to evaluation. Using NVIDIA NeMo Evaluator, they benchmark LLM performance against the ground-truth ESI labels. The evaluator runs standardized tests with custom metrics, such as a string-check to verify if the model’s output contains the correct ESI level. This setup allows for automated, repeatable testing across different models and data complexity levels. The evaluation pipeline can be integrated into CI/CD workflows, enabling continuous validation with every model update. This shift from one-off testing to ongoing assessment provides deeper insights into model behavior—such as whether a model performs well on simple cases but fails on complex, ambiguous notes. This approach transforms benchmarking from a slow, manual process into a fast, scalable, and privacy-safe workflow. It enables innovation in high-stakes domains without compromising data privacy. By combining synthetic data generation with automated evaluation, organizations can build robust, trustworthy AI systems ready for real-world deployment. The same methodology applies across industries, from financial risk assessment to government service automation, offering a scalable path forward for responsible AI development.