Chain-of-Thought Validation for LLMs

Overview — We validate LLM chain-of-thought outputs by combining expert human review, structured rubrics, and automated checks. The service evaluates reasoning traces for: factual correctness, logical coherence, relevance to prompt, unsafe or disallowed content, and hallucination risk. We produce validated labels and remediation actions suitable for fine-tuning, RLHF, benchmarks, or internal audits.

How it works — Submit a dataset of prompt → model-response → chain-of-thought samples or connect via S3/SageMaker. Our process includes: (1) sampling and stratification, (2) rubric design and reviewer training, (3) per-sample CoT annotation (scoring, flagged steps, corrections), (4) consensus/adjudication for disagreements, (5) automated checks (fact-check lookups and entity validation) and (6) deliverable packaging (annotated dataset, aggregated metrics, example corrections).

Deliverables Per-engagement delivery includes: annotated dataset (JSONL/CSV) with field-level scores and correction annotations; summary report with accuracy/factuality/hallucination metrics; per-prompt rationale review and suggested corrective prompts; confusion cases and guideline updates; audit logs and reviewer traceability.

Quality & metrics We report: factuality rate, rationale coherence score, hallucination rate, safety flag percentage, inter-annotator agreement, and examples by severity. Custom thresholds and pass/fail rules available. Integrations & formats

Output formats: JSONL, CSV, and manifest compatible with SageMaker Ground Truth. Integrates with S3, SageMaker, and common orchestration APIs (webhook/REST). Supports CoT validation, and LLM API connectors for major vendors.