LLM Output Quality Rating (Helpfulness, Safety, Factuality)

Overview: Our LLM output quality rating service offers expert human evaluation of model-generated text, focusing on essential quality attributes: helpfulness, safety, and factual correctness. Using detailed, rubric-based reviews combined with automated checks, we provide rich annotations and corrective feedback that empower teams to improve model behavior and reliability. How it works: Customers submit model outputs or integrate directly via cloud storage and API connections. Our multi-step process includes: Designing customized scoring rubrics Training human reviewers in rating helpfulness, safety risks, and factuality Per-output annotation with severity flags and corrective suggestions Consensus-driven adjudication for rating consistency Automated verification and fact-check lookups Compiling annotated datasets, comprehensive summary metrics, and examples Deliverables: Annotated output datasets (JSONL/CSV) with detailed quality ratings and corrective annotations Summary reports highlighting helpfulness scores, safety flag percentages, factuality rates, and hallucination risks Suggested refined prompts and remediation guidance for model improvement Audit logs with review traceability for transparency and compliance Quality & Metrics: Our service provides multiple granular quality indicators such as helpfulness ratings, safety compliance flags, factual accuracy percentages, and inter-rater agreement statistics. We support tailored thresholds and pass/fail criteria based on client needs. Integrations & Formats: Outputs available in JSONL, CSV, and SageMaker Ground Truth manifest formats. The service integrates smoothly with AWS S3, SageMaker, and REST/webhook APIs for automation and flexible workflows. Security & Compliance: We ensure strict security protocols, including encrypted data storage, role-based access control, and secure data handling aligned with contractual requirements. Engagement Models: Choose from one-time evaluations, iterative quality improvement cycles, or managed monthly rating services. Customers receive rubric updates, priority support, dashboards, and monthly insights as part of managed engagements.