NLG Quality Assurance — Automated Validation for Generated Text

I will build an automated quality assurance system for your NLG outputs — covering factual accuracy validation (checking generated claims against source data), fluency scoring, brand voice consistency checking, regulatory compliance language verification, and a human review queue for outputs that fail automated checks. NLG systems that generate text without automated quality validation ship errors at the rate the underlying generation system produces them — a validation layer that intercepts factual errors, off-brand language, and format violations before outputs reach users converts a probabilistic generation system into a reliable content production system with human oversight reserved for genuine edge cases.

The QA system covers fact-checking layer comparing generated statements to source data, fluency scoring using a reference model, brand voice consistency classifier, compliance language rule checker, a human review queue for flagged outputs, and a quality dashboard tracking pass/fail rates over time.