Prompt–response pair annotation

Our service provides high-quality human annotation for large language model (LLM) prompt–response pairs, focusing on validation of chain-of-thought (CoT), factual accuracy, logical coherence, and safety compliance. Designed for AI teams developing, fine-tuning, or auditing LLMs, this service delivers structured datasets that support instruction tuning, reinforcement learning from human feedback (RLHF), and regulatory benchmarking.

How It Works You can submit datasets via S3, Amazon SageMaker, or API. Our end-to-end process includes:

Stratified sampling to ensure balanced coverage across scenarios and risk domains

Custom rubric design based on model type, use case, and compliance requirements

Human annotation by trained reviewers scoring responses for factuality, coherence, relevance, and safety

Expert adjudication to resolve reviewer disagreements

Automated validation using fact-checking APIs and entity consistency tools

Dataset delivery in JSONL or CSV format with metadata, quality scores, and suggested corrections

Deliverables Each engagement includes:

Fully annotated dataset with per-field labels and confidence scores

Summary report with key quality metrics (factuality, hallucination, safety flag rate, inter-annotator agreement)

Per-prompt improvement suggestions with rationale feedback

Confusion case logs and updated rubric documentation

Audit-ready reviewer logs with traceability and timestamps

Quality and Metrics We report detailed quality indicators, including:

Factuality and groundedness in reliable sources

Logical coherence and response structure

Relevance to original prompt context

Safety and policy compliance (bias, PII, disallowed content)

Inter-annotator agreement calculated via Krippendorff’s alpha Custom quality thresholds and validation rules can be configured for each project.

Integrations and Formats Output: JSONL, CSV, SageMaker Ground Truth manifest

Integrations: Amazon S3, SageMaker, REST/webhook APIs

Supports prompt–response pairs compatible with major model APIs (OpenAI, Anthropic, Mistral, etc.)

Security and Compliance All data is handled within encrypted S3 buckets under role-based access controls. Secure data deletion is performed per contract terms. The service follows enterprise-grade data protection standards.

Engagement Models One-time Assessment: Fixed-scope validation for specified sample volumes

Iterative Annotation: Continuous labeling cycles for model improvement

Managed Validation: Monthly, SLA-backed service with monitoring dashboards and priority support