Ground truth generation for evaluation benchmarks

*Overview This service provides expertly curated ground truth data for evaluating AI models and systems. We generate and validate high-quality benchmark datasets that serve as objective evaluation standards for LLMs, vision models, and multimodal systems. Each dataset is human-reviewed, rubric-driven, and constructed to minimize ambiguity, bias, and noise while aligning with your target use cases or academic standards. How it Works Submit your task definition, example data, or model outputs via S3, SageMaker, or API. Our process includes: Task and rubric design with domain-specific guidelines Annotator training and calibration Multi-reviewer labeling and adjudication for agreement Gold-standard validation through sampling and consensus checks Automated consistency and coverage testing Final dataset assembly with confidence scores and metadata Deliverables Every engagement includes: Ground truth dataset (JSONL/CSV) with verified labels and rationale metadata Agreement and quality metrics summary Versioned benchmark report outlining accuracy, consistency, and coverage Audit logs with reviewer traceability and labeling statistics Optional comparison between model predictions and generated ground truth Quality & Metrics All benchmarks follow robust statistical and qualitative metrics, including: Inter-annotator agreement rate Labeling confidence distributions Coverage and balance scores across categories Rubric alignment and annotation consistency measures Integrations & Formats We deliver data in JSONL, CSV, or formats compatible with SageMaker Ground Truth and evaluation platforms. Supported integrations include: AWS S3 and SageMaker pipelines Evaluation API integrations for open- and closed-source models Optional packaging in benchmark-ready manifests for automated model scoring Security & Compliance Data privacy and integrity are enforced through encrypted storage, role-based controls, and customizable data handling agreements. We ensure compliance with enterprise and research data standards *