Abstract: A computer-implemented method for generating synthetic data is provided. The method includes receiving user input specifying domain-specific requirements for synthetic data generation and selecting a scenario type. The scenario type is one of Seedless, Seeded, or combination of Seeded and Knowledge Base (KB). The method defines a structured schema based on the user input. The structured schema includes data fields, relationships between data fields, and distributional targets. Based on the structured schema, the method generates an initial set of synthetic data samples using a neural template-driven generation model trained on domain-specific data. The method applies adversarial contrastive sampling. This involves training a discriminator neural network to distinguish between the initial set of synthetic samples and real data samples. The discriminator neural network is used to identify generated samples similar to real data.

Abstract: The present disclosure describes a system for detecting errors in outputs generated by GenAI models. The system includes a knowledge base of domain-specific evaluation rules and historical error patterns stored. The system parses each received GenAI output, then applies a recommendation algorithm to produce a weighted metric profile defining evaluation criteria and associated rules. Further, the system generates an embedding for the output, calibrates criterion weights by comparing the embedding to domain vectors in the knowledge base, and executes a neural network-trained on public benchmarks and enterprise-annotated examples—to yield per-criterion scores and pass-fail explanations. The processor identifies breaches by comparing scores to thresholds, creates structured error stubs, and localizes errors by projecting attention maps onto text tokens or extracting gradient-based image regions.